1,658 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].

      sentence related to color

    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.

      sentence related to color

    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.

      sentence related to color

    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].

      sentence related to color

    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.

      sentence related to any theory

    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).

      sentence related to any theory

    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.

      sentence relating to methodology

    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.

      sentence relating to methodology

    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.

      sentence relating to methodology

    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.

      sentence relating to methodology

    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.

      sentence relating to methodology

    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.

      sentence relating to methodology

    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.

      sentence relating to methodology

    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.

      sentence relating to methodology

    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.

      sentence relating to methodology

    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.

      sentence relating to methodology

    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.

      sentence relating to methodology

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

      sentence relating to methodology