SmartBook: AI-Assisted Situation Report Generation for Intelligence Analysts

The formative study aims to gather information on the general needs and expectations from intelligence analysts for AI-driven systems. The study was conducted on ten intelligence analysts with experience in government and military roles (details on recruitment in Supplementary §4.1). Over a two-week period, semi-structured interviews were conducted with these analysts to examine their understanding, perspectives, and recommendations regarding AI use in professional settings. The study highlighted emerging themes related to analysts’ perceptions and expectations of AI-assisted authoring tools, which we briefly describe below:

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  Viewing technology as a means to enhance human capability: An overwhelming majority (9 out of 10) emphasized the crucial role of AI in enhancing their capabilities, with these tools regarded not as mere process accelerators, but as essential elements that enrich their work by improving research efficiency, idea generation, and clarity of information. This perspective contrasts with the simplistic media depiction of these technologies as mere replacements for human effort.

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  Trusting and relying on machines, as with humans: The majority of participants (8 out of 10) exhibited a tendency to attribute human-like qualities of trust and reliability to AI systems. The criteria for trusting AI closely resembled those for human interactions: the ability to provide dependable information, transparency in reasoning, and a foundation in verifiable facts. Interestingly, analysts did not set higher standards for AI than for human colleague. This parity in trust and reliability criteria suggests that participants viewed AI as an equal collaborative partner, assessing its competence and trustworthiness on the same grounds as a human team member.

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  Training and guiding AI: Our study identified a split in intelligence analysts’ perspectives on their role in training and guiding AI systems. Four out of ten participants advocated for substantial control over AI, emphasizing the need for an interactive system that allows them to influence everything from information source selection to narrative shaping in reports. In contrast, the majority (six out of ten) favored a more hands-off approach, highlighting that situation report creation follows well-established, standardized procedures suitable for AI implementation. They perceived AI involvement as an extension of routine oversight, akin to reviewing a junior colleague’s work.

To gain an operational understanding of intelligence analysis process and generating situation reports, we expanded the design opportunities identified in the formative study (§2.1.1) with subsequent collaborative design sessions with the ten analysts. The goal was to capture tangible design strategies and recommendations from users about how they, as intelligence analysts, navigate, research, and author their situation reports.

We conducted study sessions with the analysts, with each session consisting a workflow review using storyboards and hands-on training with a simulation report exercise. In our study, participants engaged with a low-fidelity storyboard (shown in Figure 2), where each panel depicted a distinct phase in situation report creation. Participants were tasked with providing detailed descriptions of each storyboard panel to ensure comprehension of the depicted scenario and workflow. Participants simulated each storyboard step using sample situations to gain practical workflow experience, and were advised to utilize diverse resources, including web search engines like Google and Bing, and Large Language Models (LLMs) such as ChatGPT (Achiam et al., 2023), for task completion.

Data analysis from the collaborative design sessions showed three main themes: enhancing analytical efficiency, transparency in AI systems, and customization flexibility. Participants highlighted the need for interfaces that reflect their mental models, reducing cognitive load and allowing them to focus on strategic aspects. There was a significant emphasis on understanding AI systems’ underlying logic for trust, with a preference for transparent methods and traceable data sources to verify the credibility of automated outputs. Additionally, users expressed a desire for tools that support varying analytical styles and complexities, and that can integrate information from diverse sources to provide a comprehensive analysis.

From the findings of the formative study and the collaborative design above, we identified the following design strategies:

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  DS1: Given the emphasis on reducing cognitive load and enhancing analytical efficiency, the system will be designed with an interface, that mirrors intelligence analysts’ natural processes of data analysis and report generation.

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  DS2: To increase efficiency, the system will integrate features to automate time-intensive tasks such as question curation and preliminary research, thereby reducing analysts’ manual workload and enabling greater focus on strategic analysis and decision-making.

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  DS3: The design, addressing the need for trust and reliability, will convey clear explanations of the system’s data processing algorithms and criteria. This includes transparent data sourcing, providing references within reports, and tools for users to easily understand and verify the system’s conclusions. The design will also facilitate incremental trust-building through consistent and validated performance over time.

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  DS4: Addressing the themes of customization and flexibility, the system will offer a high degree of adaptability to accommodate various analytical styles and levels of detail in reporting. It will include features for adjusting the depth of analysis, focusing on specific data sets, and seamlessly integrating various data sources.

Situation reports cover event progressions over considerably long periods. Hence, it is beneficial to organize such reports in the form of timelines (F1 in Fig. 3), which enables seamless report updates (Ma et al., 2023) with new events and helps facilitate (Singh et al., 2016) users tracking and understanding of situation context (informed by DS1). Timelines aid intelligence analysts in understanding event progression and predicting future trends by organizing events chronologically and highlighting cause-and-effect relationships. For readers, especially those less familiar with the subject, timelines provide a visual guide to easily grasp the sequence and significance of events in a scenario. Our automatic situation report has timelines to provide a coherent, chronological representation of event developments (DS1, DS2).

In each timespan, we first identify major events by clustering daily news summaries from the period into major event groups using an agglomerative hierarchical clustering algorithm (Jain and Dubes, 1988) based on term frequency-inverse document frequency (TF-IDF) scores (Sparck Jones, 1972). Finally, we are left with clusters of news snippets, each providing a focused view of a major event. However, since news summary snippets are condensed in detail, we improve the comprehensiveness of each event cluster by expanding the news corpus, as described next. First, to create a chapter name for SmartBook and also use it for additional news article retrieval, we generate a concise headline for each event cluster. To achieve this, we utilize a sequence-to-sequence transformer-based (Vaswani et al., 2017) language model, BART (Lewis et al., 2020), that takes the concatenated title and text from all the news snippets within the event cluster as input and generates a short event heading. We made use of the model trained on the NewsHead dataset (Gu et al., 2020) for generating multi-document headlines. In that dataset, each cluster contained up to five news articles, and a crowd-sourced headline of up to 35 characters described the major information in that cluster. We then use these chapter names as the query to retrieve additional relevant news articles via Google News.

A situation report should have a logical structure and descriptive section titles (F2 in Fig. 3) for clarity and easy access to information for intelligence analysts (DS2). SmartBook not only describes event details in each chapter but also aims to present strategic insights that support decision-making and policy planning. To guide such detailed analysis, we incorporate a logical structure by automatically generating section headings in the form of strategic questions relating to each major event. These questions cover insightful details such as the motivations behind actions in an event and its potential future consequences.

Recent work (Sharma, 2021; Wang et al., 2022a) has shown that LLMs are capable of generating comprehensive, natural questions that require long-form and informative answers, in comparison to existing approaches (Murakhovs’ka et al., 2022; Du et al., 2017) that mainly generate questions designed for short and specific answers. In this work, we direct the LLM (GPT-4) to generate strategic questions about specific events, using news articles from the event cluster to anchor the context and reduce model-generated hallucinations (Ji et al., [n. d.]; Maynez et al., 2020). To ensure diversity in the generated questions, we sample multiple question sets using nucleus sampling (Holtzman et al., 2019). Our analysis reveals that questions may occasionally be repeated across different sets, as depicted in Figure 4 where duplicates are highlighted in blue. To address this, we perform question de-duplication using a RoBERTa-large (Liu et al., 2019) model trained on the Quora Duplicate Question Pairs dataset, thereby consolidating the sets into a singular, diverse collection of strategic questions relevant to the event.

Intelligence analysts, given the high stakes nature of their work but limited time, need systems that quickly identify key information in documents (DS2). This enables them to focus on urgent matters without sorting through irrelevant data. Hence, automated situation report generation should be able to identify and extract the most scenario-relevant and crucial information across multiple documents (F5 in Fig. 3). Readers of the situation report benefit from information salience because they are presented with a concise, relevant overview of a situation. Essential points need to be highlighted, to enhance readability and clarity. Moreover, we also present the bias of each news source (taken from AllSides) to help analysts consider information presented from different perspectives.

Providing readers with a comprehensive understanding of event context requires foraging for different claims and hypotheses from the source documents (i.e., news articles) that help explain a situation (Toniolo et al., 2023). We adopt a Question Answering (QA) formulation to identify claims relevant to a given strategic question, driven by the ability of directed queries to systematically extract relevant claims from news articles, as demonstrated in recent research (Reddy et al., 2022b, a). Our QA pipeline utilizes a transformer-based RoBERTa-large encoder model (Liu et al., 2019) that has been trained on SQuAD 2.0  (Rajpurkar et al., 2018) and Natural Questions  (Kwiatkowski et al., 2019). The model takes as input the news corpus split into snippets along with the strategic question, and outputs answer extractions to these questions. The corresponding sentences that contain these answers are considered as the extracted claims. However, the risk of high-confidence false positives (Chakravarti and Sil, 2021) necessitates validation (Reddy et al., 2020; Zhang et al., 2021) of these answers. To address this, we utilize an answer sentence selection model (Garg et al., 2020) that verifies each context against the strategic question. The model is a binary classifier with a RoBERTa-large backbone trained on datasets such as Natural Questions (Kwiatkowski et al., 2019) and WikiQA (Yang et al., 2015), and outputs a validation score ranging from 0 (incorrect) to 1 (correct), which is used to select the top-5 relevant contexts for summarization.

Considering the issue of hallucination in LLM-based summarization (Ji et al., 2023; Li et al., 2023a; Bang et al., 2023), factuality is far more important than creativity for situation report generation. A reliable situation report must be anchored in verifiable sources to ensure credibility (DS3). This supports analysts in drawing robust, evidence-based conclusions, and the embedded links act as a springboard to more extensive research for readers wanting to dive deeper (F6, F7, F8 in Fig. 3). Additionally, we offer summaries with varied detail levels—brief (2-3 sentences), standard (4-6 sentences), and extended (2 paragraphs)—to cater to different reader preferences (F4 in Fig. 3) (DS4).

Using strategic questions obtained for each chapter as section headings, we incorporate query-focused summarization to generate each section’s content. A concise summary is generated for each section in SmartBook based on the relevant claim contexts (F3 in Fig 3). Recent work (Goyal et al., 2022; Bhaskar et al., 2022; Reddy et al., 2022c) has shown that humans overwhelmingly prefer summaries from prompt-based LLMs (Brown et al., 2020; Chowdhery et al., 2022) over models fine-tuned on article-summary pairs (Lewis et al., 2020; Zhang et al., 2020; Liu et al., 2022), due to better controllability and easier extension to novel scenarios. For summary generation, we feed the LLM (GPT-4) with the top-5 most relevant contexts (from §2.2.3) and instructions to summarize with respect to the given strategic question and include citations. This approach not only aids in maintaining accuracy by grounding on claim contexts but also enhances the trustworthiness of the summaries by allowing verification against the cited sources.

The structured layout of the user interface (UI) was notably effective, as seen in the free-form investigation (ii) with a 100% feature discovery rate (10 out of 10 participants). All participants successfully engaged with key features, underscoring the UI’s design intuitiveness. This high feature discovery rate indicates that the UI aligns with users’ cognitive patterns, facilitating intuitive interaction without extensive training, providing us with a positive response to RQ1. This sentiment was further emphasized in the post-study questionnaire. As seen in the results in Figure 5, 70% of participants strongly agreed that most intelligence analysts would learn to use the system very quickly, while 80% strongly disagreed that the system was difficult to navigate and use. However, we observed mixed views on UI flexibility, specifically in personalization and customization. While most found the tool easily integrateable into their workflow, a minority (30%) remained neutral. When further prompted, two participants described the UI as a “one size fits all” solution, lacking personalization.

In the study, trust in the SmartBook system developed progressively, resembling the formation of trust in a human analyst. Initially, users exhibited skepticism, thoroughly scrutinizing the system’s sources and assertions. This included validating the authenticity of sources, the accuracy and pertinence of ratings, and the correct representation of context. As users became more acquainted with SmartBook and evaluated its reliability, their dependence on extensive source verification lessened. As can be seen in Figure 5, three out of ten participants did not feel the need to conduct additional research beyond the presented information to trust the tool, while another three felt additional research was necessary. This trust was found to be context-dependent, with two participants noting that their trust varied based on the “impact severity” or the potential negative consequences of disseminating incorrect information. Nonetheless, a majority (eight out of ten) concurred that the information provided was accurate and reliable, a positive answer to RQ2.

The primary benefit identified was the substantial reduction in time and effort required for compiling and analyzing complex data. SmartBook’s automation of initial report generation processes, such as data collection and summarization, was highly valued by analysts as it allowed more focus on thorough analysis and strategic planning. As seen in Figure 5, all participants agreed on the tool’s utility in assisting intelligence analysts in creating situation reports, expressing satisfaction with the system-generated reports, thereby providing a positive response to RQ2. Regarding the necessity of significant edits to the system-generated reports, half participants suggested that this need was not inherently due to report deficiencies but varied based on the report’s intended purpose or audience. We later conducted a study (described in §3.3) to understand the extent of edits needed.

Decision-makers highly valued SmartBook’s ability to rapidly deliver accurate and easily digestible information. Their primary engagement with the interface centered around utilizing the summaries, to learn about different topics in various degrees. The system’s effectiveness in simplifying complex data into structured, clear formats was particularly appreciated, to help aid in swift understanding and decision-making processes. Although data lineage and source transparency were recognized as important, these were considered secondary to the primary need for timely and format-specific information delivery. While SmartBook’s target users are intelligence analysts, the decision-makers’ view highlights SmartBook’s dual functionality as both an analytical tool and a decision-support system, providing a key capability for the high-paced and information-intensive needs of decision-makers, addressing RQ3.

Evaluation of the quality of strategic questions, which are used as section headings in SmartBook, is done by drawing parallels to the traditional construction process of a situation report, which involves the participation of both senior and junior analysts. A senior analyst’s role is usually to come up with strategic questions, which are then passed onto a junior analyst. The junior analyst then gathers tactical information that can help answer or provide more background to the strategic questions. Our human evaluation of the questions within SmartBook measures the following aspects, based on guidelines defined by the expert intelligence analyst:

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  Strategic Importance: Evaluating the strategic importance of a question requires introspection on whether the question provided within SmartBook probes for an insightful aspect of the event. The scoring rubric involves marking the question as one of three categories, namely, ‘Not Strategic’, ‘Some Strategic Value’, and ‘Definitely Strategic’. This metric is based on the level of speculative nature, such as questioning the background/motives/reasons behind actions taken by actors (governments/military, etc) involved in the event.

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  Tactical Information: Another dimension of evaluating the quality of a question is how much relevant tactical information can be gathered using that question. In this case, the question is evaluated based on its corresponding SmartBook summary, in terms of whether the question-relevant tactical information in the summary is helpful for an analyst to gain deeper insights into the situation. Tactical information is defined as content that is neither obvious nor trivially obtained (e.g. cannot be obtained from a news header). The scoring rubric is categorized as ‘No information is tactical’, ‘Some information is tactical’, and ‘Most information is tactical’. Table 6 in supplementary shows examples for summaries with corresponding tactical information.

We randomly selected 25 chapters from SmartBook, comprising 125 strategic questions, with each question evaluated by three annotators. Results show that most questions are strategic (detailed split in Figure 7 in supplementary), with at least 82% having some strategic value. Further, we see that these questions can help gather relevant tactical information in roughly 93% of the cases, as judged by the annotators that most summaries to these questions have tactically relevant information. When assessing the diversity of strategic questions within each chapter, evaluators noted that 64% of chapters contained questions addressing different aspects, 28% had up to two questions on similar aspects, and 8% included more than two questions on similar aspects.

Next, we assess the quality of text summaries generated by SmartBook, which uses question-driven claim extraction to identify and summarize relevant information. For baseline comparisons, we utilize two methods: firstly, a query-focused summarization baseline that processes entire news articles directly with an LLM to generate summaries for the strategic question as the query. This approach uses the same LLM and prompt as in SmartBook, except the entire news article texts are passed as input context without an explicit claim extraction step. Secondly, we compare against a web search + LLM baseline, where relevant web pages sourced from the internet serve as the input context, which are then summarized by an LLM. This method simulates using an LLM-enabled web search engine (like perplexity.ai), incorporating the strategic question as the query along with a phrase “concerning the Ukraine-Russia crisis between $<$timeline$>$”. Since the LLM-enabled web search was using GPT-3.5 (OpenAI, 2021) at the time of experiments, we used GPT-3.5 as the LLM in SmartBook and other baselines for a fair comparison. We randomly select summaries corresponding to 50 strategic questions from SmartBook to perform the evaluation.

For each generated summary, three evaluators are asked to assess the coherence, relevance, and usefulness with scores ranging from 1 (worst) to 5 (best). Simply, coherence measures the quality of all summary sentences collectively regardless of the question. Relevance quantifies whether the summary contains key points that answer the given question. We define usefulness as an indication of whether the summary provides non-trivial and insightful information for analysts, and suggests the breadth and depth of the provided key points. Table 2 shows results from the evaluation study. We observe that SmartBook outperforms alternative competitive strategies in coherence, relevance, and usefulness. The advantage of the question-driven claim extraction in SmartBook is evident, yielding significantly more relevant summaries compared to the direct query-focused summarization without such a step (row (3) vs (1)). Additionally, summaries generated using information sourced from the Web are less useful than those generated through SmartBook’s news-driven approach (row (2) vs (3)). Table 3 displays an example showing the difference in outputs. Overall, the evaluation demonstrates that across the metrics, SmartBook excels over the baselines in providing high-quality summaries.

We also evaluated the citation quality in SmartBook’s summaries using metrics for citation precision and recall as defined by Gao et al. (2023b). Citation recall assesses whether the output sentence is fully supported by the cited context, whereas citation precision identifies any irrelevant citations. These metrics were calculated using the 11B-parameter TRUE model (Honovich et al., 2022), which is trained on a collection of natural language inference datasets, and is frequently employed to evaluate attribution accuracy (Bohnet et al., 2022; Gao et al., 2023a). Overall, 97% of the sentences in SmartBook’s summaries included citations, while 29.5% had multiple citations. We observed a citation precision and recall of 64.7% and 69.2%, respectively.

We have developed SmartBook, an innovative framework that generates situation reports with comprehensive, current, and factually-grounded information extracted from diverse sources. SmartBook goes beyond mere information aggregation; it presents chronologically ordered sequences of topically summarized events extracted from news sources and placed into a UI layout structure that aligns with the workflow of intelligence analysis. Throughout the development of SmartBook, from its conceptual design to its evaluation, we engaged analysts to ensure the tool meets their practical needs and enhances their workflow. Our formative study and collaborative design efforts have been guided by the needs of intelligence analysts, particularly in addressing the challenge of data overload. The findings from these studies indicate that analysts are cautiously optimistic about integrating AI assistance into their processes, expressing a clear desire to shape a future in which AI tools can both adapt to the evolving data landscape and personalize to their individual analytical techniques.

SmartBook establishes a foundation for situation understanding in a variety of scenarios. However, history repeats itself, sometimes in a bad way. Historical patterns, particularly those leading to natural or man-made disasters, can be a useful signal to trigger proactive measures for crisis mitigation. Building on prior research in event prediction (Li et al., 2021; Wang et al., 2022b; Li et al., 2023b), SmartBook can guide the development of a news simulator that forecasts event outcomes in crisis situations. This tool would be invaluable for humanitarian workers and policymakers to exercise reality checks, enhancing their capacity to prevent and respond to disasters effectively.

While SmartBook represents a significant advancement in the automated generation of situation reports, it is essential to acknowledge certain limitations that stem from both the technical aspects of the system and the scope of its application. These include (i) unverified news source credibility, (ii) potential inaccuracies in reflecting source material despite citations, and (iii) user studies focused mainly on military intelligence analysts, which may not represent the needs of a wider analytical audience. Recognizing these limitations is crucial for guiding future improvements and ensuring the framework’s applicability across diverse intelligence analysis sectors. We elaborate on the limitations in more detail in the supplementary material. Here, we provide key future extensions designed to elevate SmartBook’s utility as a comprehensive, unbiased, and reliable tool for situation report generation in intelligence analysis:

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  Incorporating Multimodal, Multilingual Information: Intelligence analysis increasingly relies on integrating diverse data types such as text, images, videos, and audio to understand global events comprehensively. We aim to enhance SmartBook’s situation reports by correlating textual claims with corresponding multimedia, utilizing advanced systems like GAIA (Li et al., 2020) for multimedia knowledge extraction. Furthermore, global events’ reach necessitates multilingual intelligence analysis to capture local nuances missed by solely using English. Employing cross-lingual techniques (Du et al., 2022) allows for the inclusion of multiple languages, thereby enhancing understanding of local dynamics and cultural subtleties that influence global situations. Thereby, integrating multilingual sources democratizes intelligence analysis, shifting away from a predominantly English-centric perspective. Ultimately, SmartBook aims to set new standards in intelligence reporting, fostering a more inclusive and globally informed approach.
  

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  Controlling the Bias of News Sources: News sources carry inherent biases influenced by editorial policies, audience demographics, and geographical locations. Dominance of a single perspective may obscure crucial details or alternative viewpoints. In developing the next version of SmartBook, we seek to mitigate these biases by incorporating a diverse spectrum of news outlets representing various political stances. This approach aims to diminish informational blind spots and expand the scope of scenarios available to intelligence analysts, thereby enhancing SmartBook’s utility.
  

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  Co-Authoring with Iterative Refinement: Authoring situation reports is an iterative process, where analysts continuously refine and update the reports. In the next iteration, we aim to provide a dynamic multi-turn editing process with SmartBook, by leveraging analyst feedback for self-improvement. Techniques like Reinforcement Learning with Human Feedback (Stiennon et al., 2020; Bai et al., 2022) and other personalization algorithms (Wu et al., 2019; Monzer et al., 2020) will further enhance the system’s capability to integrate human feedback, and progressively adapt to the preferences and decision-making styles of analysts. This strategy will align SmartBook more closely with human analysts, leading to tailored situation reports that meet the specific requirements of intelligence analysis workflows. Ultimately, SmartBook will evolve from a preliminary drafting tool to a comprehensive AI co-author for generating situation reports.
  

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  Improving Reliability of the Generated Reports: The integrity of data and claims in situation reports is crucial for strategic decision-making. A key challenge in automating these reports is guaranteeing the accuracy and reliability of data from diverse sources. To address this, we propose introducing a ‘verification score’ for each claim, which evaluates the reliability of information based on source credibility, corroborative data, and historical accuracy of similar claims. This mechanism provides intelligence analysts with a confidence metric to quickly assess data reliability. Higher scores indicate greater confidence and facilitate rapid integration into reports, whereas lower scores necessitate comprehensive review and possibly further verification.