4,201 Matching Annotations
  1. Mar 2026
    1. The Posi-tional Diction Clustering (PDC) algorithm identified analogous sentences across many LLM responses, which were reified both as color-coordinated cross-document analogous text highlighting (like ParaLib) and in a novel ‘interleaved’ view where analogous sen-tences across documents were rendered in adjacent rows to enable more easy comparison [18].

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    2. The Semantic Reader project [43] supports features that bring information from related papers into the focal paper’s reading environment. For example, Relatedly [54], part of the Semantic Reader project, highlights unexplored dissimilar information in related work sections of unread papers while low-lighting previously seen information.

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    3. For example, GP-TSM [24] helps readers read more efficiently by modulating text saliency while preserving grammar. Varifocal- Reader [36] supports skimming by presenting abstract summaries alongside the source document, with machine-learned annotations highlighting key sentence segments in different colors.

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    4. The Positional Diction Clustering (PDC) algorithm identified analogous sentences across many LLM responses, which were reified both as color-coordinated cross-document analogous text highlighting (like ParaLib) and in a novel ‘interleaved’ view where analogous sentences across documents were rendered in adjacent rows to enable more easy comparison [18].

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    5. AbstractExplorer instantiates new minimally lossy2 SMT-informed techniques for skimming, reading, and reasoning about a corpus of similarly structured short documents: phrase-level role classification that drives sentence ordering, highlighting, and spatial alignment.

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    6. Structural Mapping Theory (SMT) is a long-standing well-vetted theory from Cognitive Science that describes how humans attend to and try to compare objects by finding mental representations of them that can be structurally mapped to each other (analogies).

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    7. In the context of close reading of research paper abstracts at scale, our findings suggest AbstractExplorer enabled participants to scale up the number of papers they could review through efficient skimming and find common patterns and outliers through sentence comparison, resulting in a rich synthesis of ideas and connections to foster deeper engagement with scholarly articles.

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    8. We extend existing approaches through automated role annotation, establishing alignments using grammatical chunk boundaries, and preserving sentences in their entirety, instead of relying on abstract meta-data.

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    9. In this work, we introduce a new paradigm for exploring a large corpus of small documents by identifying roles at the phrasal and sentence levels, then slice on, reify, group, and/or align the text itself on those roles, with sentences left intact.

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    10. Custom aspects are generated dynamically via API calls to a FastAPI back-end, which prompts an LLM to check whether each sentence in the filtered subset matches the aspect description—either in terms of overall content or a matching token—and extracts the most relevant chunk of that sentence to highlight.

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    11. After obtaining an expanded set of high-level chunk labels, we assign them to each of the sentence chunks by using LLMs in a multi-class classification few-shot learning task, with the initial labels and assignment as examples.

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    12. After identifying chunk boundaries, we again prompt an LLM to generate labels for chunks in a human-in-the-loop approach: starting from an initial set of labels for chunk roles, when a new label is generated, a researcher from the research team examines the new label and merges it with existing labels if appropriate, controlling for the total number of labels.

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    13. In the first stage, Sentence Segmentation and Categorization, abstracts are split into individual sentences using the NLTK package, and each sentence is classified into one of the five pre-defined aspects as listed in Section 4.1.1.

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    14. When users click on a bookmark icon to the left of any specific sentence in the Cross-Sentences Relationships Pane, that sentence is added to a bookmark list that can be viewed in the Bookmarked Sentences alternate pane.

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    15. Filtering enables users to narrow their focus to a subset of the corpus while still benefiting from features that help them recognize cross-sentence relationships within the remaining abstracts.

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    16. The Abstracts panel can be customized by users to display the full abstract text, an abstract “TLDR” (a shorter abstractive summary generated by an LLM), or both at the same time.

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    17. To allow users to contextualize individual sentences within their respective abstracts, we link the Cross-Sentence Relationship and Abstract panels: when users click on any sentence in the Cross-Sentence Relationships pane, the corresponding full abstract is automatically highlighted and scrolled into view in the Abstracts panel, offering additional context when needed.

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    18. Together, the vertical and horizontal juxtapositions are designed to help users identify both high-level commonalities and nuanced variations across structurally similar sentences.

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    19. These alignment options are intended to enable users to more easily read analogous chunks across sentences from different abstracts, ignoring details serving other roles within the sentence.

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    20. By default, sentences are vertically aligned by the middle of their shared structure tuple, but users can freely switch between the three alignment options using the button group atop the Cross-Sentence Relationship pane.

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    21. AbstractExplorer also aligns the sentences in three different ways, as illustrated in Figure 5: vertical alignment by the middle of the structure tuple (second element), vertical alignment by the left of the structure tuple (first element), and left-justified alignment (horizontal juxtapositions).

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    22. This ordering prioritizes dominant structural patterns (largest groups first) while exposing fine-grained variations (via length-sorted triplets), mirroring how humans compare sentences, if SMT is an accurate description in this domain of comparative close reading.

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    23. This allows users to first understand the different structure patterns and their commonality, before diving into close reading at scale of the sentences that share a particular structure by clicking any of the “Expand” toggles.

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    24. AbstractExplorer first segments sentences into grammar-preserving chunks—segments that respect grammatical boundaries, i.e., an LLM judges that the sentence can be truncated at that chunk boundary without breaking the grammatical integrity of the preceding text.

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    25. Viewing one aspect at a time enables users to closely read and compare just the analogous sentences of abstracts, which may be cognitively easier than the comparative close reading of many abstracts in their entirety, especially if cross-sentence relationships are pre-computed and reified in the interface.

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    26. AbstractExplorer classifies sentences into five pre-defined aspects common in CHI abstracts: Problem Domain, Gaps in Prior Work, Methodology/Contribution, Results/Findings, and Discussion/Conclusion.

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    27. We chose the sentence as our unit for cross-document alignment because: (1) it preserves complete propositional content (unlike phrases or words), (2) maintains grammatical coherence when isolated (unlike arbitrary text spans), and (3) serves as the minimal self-contained unit where aspects can be meaningfully compared.

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    28. To keep details at the forefront of the interface, we designed a mechanism to slice abstracts for viewing them from specific angles, allowing for comparative close reading at scale at the sentence level.

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    29. ABSTRACTEXPLORER is designed to help researchers (1) skim, read, and better familiarize themselves with the contents and composition style of a large corpus of abstracts and (2) reason about cross-paper relationships at scale without abstracting away the author-written sentences about their own work.

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    30. Finally, a summative study (Section 6) describes how researchers used ABSTRACTEXPLORER to familiarize themselves with a corpus of ~1000 CHI paper abstracts—reading across a larger and more diverse collection of abstracts and more easily discerning relationships and distributions across prior work.

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    31. Second, an ablation study with eye-tracking (Section 5) revealed that the three key features of ABSTRACTEXPLORER's central cross-sentence relationships pane-sentence order, role-coordinated highlighting, and alignment-work best in concert, not alone.

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    32. Three studies inform and validate ABSTRACT EXPLORER's design: First, a formative study (Section 3) suggested unmet needs and interest in our approach to supporting cross-document reasoning.

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    33. AbstractExplorer instantiates new minimally lossy SMT-informed techniques for skimming, reading, and reasoning about a corpus of similarly structured short documents: phrase-level role classification that drives sentence ordering, highlighting, and spatial alignment.

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    34. A summative study (N=16) describes how these features support users in familiarizing themselves with a corpus of paper abstracts from a single large conference with over 1000 papers.

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    1. An appealing alternative to conventional text-based interfaces through graphical user interfaces is the direct use of hands as an input device to provide natural human-computer interaction.

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    2. A more thorough description of the current tools and techniques for interacting with computers as well as recent developments in the subject is provided in the next section.

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    3. The evolving multi-modal and Graphical user interfaces (GUI) enable humans to interact with embodied character agents in a way that is not possible with other interface paradigms.

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    4. The widely used graphical user interfaces (GUI) of today are found in desktop applications, internet browsers, mobile computers, and computer kiosks.

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