To address this, we conducted a systematic review and meta-analysis of 56 papers p.

Users can enter a search term into the search bar and only papers that include that exact term will appear in the Cross-Sentence Relationships pane.

Through two user studies, we found that with an accompanying size-ch.

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.

Through a VR experiment (N=31) and a following online survey (N=142).

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.

We conducted five user studies with 98 participants.

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.

methods, we conducted a controlled experiment (N=24) to assess the user performance, experience and VR sickness.

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

ryday life, we ran three co-design workshops (N=69).

If users prefer, sentences can be toggled to a left-justified view to facilitate conventional reading and skimming.

In our mixed-method study with 21 participants, we evaluated the usability and effectiveness.

This may effectively decrease context switching and lead to more robust mental models without requiring more cognitive load.

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.

ucted on eight-month longitudinal user study with 12 older adults aged 60 years and above to demonstrate the eff.

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.

In a study (N=18), we compared our multimodal technique with pseudo-haptics al.

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).

Through a study with 30 participants, we show that the system enables users to perc.

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.

ploratory and a confirmatory factor analysis with a total of 159 participants.

Within each of those groups, sentences are arranged by increasing length.

We conducted an experiment with 24 participants using a real vehicle along a route with known.

Sentences are recursively grouped by sequences of three chunk roles, with groups ordered by decreasing size.

We conducted a study involving 32 participants organized in teams of two.

AbstractExplorer orders sentences within each structure group based on the sequential pattern of chunk roles (vertical juxtapositions).

In a within-subjects study (20 participant pairs), we examined the effects of vibrotactile hap.

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.

Through task-based experiments with 25 pairs of participants (50 individuals), we evaluated quanti.

An example sentence is also shown alongside each structure.

The total number of sentences with each structure in the (possibly filtered) corpus is shown in parentheses and visualized as a histogram.

In AbstractExplorer, sentences are grouped by this definition of sentence structure.

These role-based color highlights enable quick identification of analogous chunks and visual pattern matching over sequences of chunks across sentences.

Each role label has a corresponding unique color shown at the top of the “Cross-Sentence Relationship” panel.

We adopt the color palette of Tableau 10, which was carefully designed to be clearly distinguishable.

Each chunk is then classified by an LLM as having one of nine pre-defined roles, each of which has its own assigned color.

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.

We conducted five user studies with 98 participants.

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.

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.

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.

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.

To do this, ABSTRACTEXPLORER is designed to support close-reading purpose-defined slices through a large collection-1,057 in our studies-of paper abstracts.

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.

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.

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.

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.

We demonstrate these features’ utility in the context of helping researchers’ skim and read closely and laterally across a corpus of scientific abstracts.

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.

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.

An ablation study (N=24) validated that these features work best together.

AbstractExplorer has a unique combination of LLM-powered (1) faceted comparative close reading with (2) role highlighting enhanced by (3) structure-based ordering and (4) alignment.

We compose and evaluate a system, called AbstractExplorer, with analogous SMT-derived characteristics for the domain of scientific abstract corpus familiarization.

The ablation and summative studies verified the value of Abstract-Explorer, specifically showing that all three components of the Structural Mapping Engine—color coding, sentence ordering, and vertical alignment—are crucial for facilitating comparative close reading at scale.

The study concluded with a 15-minute semi-structured interview. During the interview, participants saw screenshots from the three conditions and were asked which they preferred and disliked, why, what they wished the interface had, what influenced their skimming, and how they normally skimmed texts.

After the ablation study validated the effectiveness of all three SMT-inspired features together (especially for lower NFC users), we completed the implementation of AbstractExplorer and eval-uated its impact on researchers’ reading and sensemaking of a corpus of all ∼1000 paper abstracts from ACM CHI 2024.

The most preferred condition (all three features enabled) was tied with the baseline no-features-enabled condition for lowest reported cognitive load. Specifically, 11 participants reported their lowest raw NASA-TLX scores8 in the all-three-features condition, and a different 11 participants reported their lowest raw NASA-TLX scores in the baseline condition.

The most popular condition had all three features enabled, i.e., 11 out of 24 participants (≈ 50%) preferred Figure 7C, as shown in the “Preferred” columns of Table 1. The remaining participants were roughly evenly split between the no-features baseline (6 par-ticipants) and the without-alignment ablation condition (5 partic-ipants). One participant each liked the without-highlighting and without-ordering ablation conditions most, respectively.

The specific research questions for this study were: (1) How do highlighting, alignment, and ordering affect reading patterns, user experience, and cognitive load? (2) How do participants’ valuation of these features relate to their Need for Cognition? (3) Does each feature provide value on its own, or only in conjunction with one or more of the other two features?

In this study, we allowed participants to experience views of same-aspect sentences (Section 4.1.1) with different combinations of high-lighting, ordering, and alignment (as described in Section 4.1.2 and Section 4.1.4) enabled or not, in order to understand which and/or what combinations most effectively supported users’ ability to skim and read laterally across documents.

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. 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 high- lighting, and alignment-work best in concert, not alone. Finally, a summative study (Section 6) describes how researchers used AB- STRACTEXPLORER to familiarize themselves with a corpus of ~1000 CHI paper abstracts—reading across a larger and more diverse col-lection of abstracts and more easily discerning relationships and distributions across prior work.

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.

An ablation study (N=24) validated that these features work best together.

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 high- lighting, and alignment-work best in concert, not alone.

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 high- lighting, and alignment-work best in concert, not alone.

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 high- lighting, and alignment-work best in concert, not alone.

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 high- lighting, and alignment-work best in concert, not alone.

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.

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 high- lighting, and alignment-work best in concert, not alone.

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 high- lighting, and alignment-work best in concert, not alone.

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 high- lighting, and alignment-work best in concert, not alone.

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 high- lighting, and alignment-work best in concert, not alone.

AbstractExplorer used variation affordances present in prior systems, e.g., color-coordinated highlighting of analogous text in Gero et al. [18], and introduced new ones, such as alignment of sentences based on analogous chunks within them, which had only been hypothesized in prior work.

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).

AbstractExplorer instantiates new minimally lossy SMT-informed techniques for skimming, reading, and reasoning about a corpus of similarly structured short documents.

Lossless SMT-informed techniques have yet to be brought to bear in the context of researchers familiarizing themselves with a corpus of existing literature.

This SMT-informed approach, which AbstractExplorer shares, tries to give this mental machinery “a leg up,” letting users perhaps skip some steps by accepting reified cross-document relationships identified by the computer.