GLIMPSE: Pragmatically Informative Multi-Document Summarization for Scholarly Reviews
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Abstract (arXiv)
Scientific peer review is essential for the quality of academic publications. However, the increasing number of paper submissions to conferences has strained the reviewing process. This surge poses a burden on area chairs who have to carefully read an ever-growing volume of reviews and discern each reviewer's main arguments as part of their decision process. In this paper, we introduce sys, a summarization method designed to offer a concise yet comprehensive overview of scholarly reviews. Unlike traditional consensus-based methods, sys extracts both common and unique opinions from the reviews. We introduce novel uniqueness scores based on the Rational Speech Act framework to identify relevant sentences in the reviews. Our method aims to provide a pragmatic glimpse into all reviews, offering a balanced perspective on their opinions. Our experimental results with both automatic metrics and human evaluation show that sys generates more discriminative summaries than baseline methods in terms of human evaluation while achieving comparable performance with these methods in terms of automatic metrics.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 21 of its 43 references that are in the map. First posted on arXiv 11 Jun 2024.
Builds on
21 of 43 references.
References · 21
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9 more
- 2019Pragmatically Informative Text Generationcited by 32
- 2019Informative and Controllable Opinion Summarizationcited by 27
- 2020Unsupervised Opinion Summarization with Content Planningcited by 27
- 2020A Rate-Distortion view of human pragmatic reasoningcited by 19
- 2023Scientific Opinion Summarization: Paper Meta-review Generation Dataset, Methods, and Evaluationcited by 14
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Built on by
Citing papers per year: 2024: 1; 2025: 3; 2026: 2.
6 papers.
- 2025From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systemscited by 8
- 2025DSAS: A Universal Plug-and-Play Framework for Attention Optimization in Multi-Document Question Answeringcited by 3
- 2025Collaborative Rational Speech Act: Pragmatic Reasoning for Multi-Turn Dialogcited by 2
- 2026When AI reviews science: Can we trust the referee?cited by 2
- 2024Does This Summary Answer My Question? Modeling Query-Focused Summary Readers with Rational Speech Actscited by 0
- 2026MVSS: A Unified Framework for Multi-View Structured Survey Generationcited by 0
- 2026When AI reviews science: Can we trust the referee?26 Apr 2026
- 2026MVSS: A Unified Framework for Multi-View Structured Survey Generation14 Jan 2026
- 2025DSAS: A Universal Plug-and-Play Framework for Attention Optimization in Multi-Document Question Answering14 Oct 2025
- 2025Collaborative Rational Speech Act: Pragmatic Reasoning for Multi-Turn Dialog18 Jul 2025
- 2025From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems3 Mar 2025
- 2024Does This Summary Answer My Question? Modeling Query-Focused Summary Readers with Rational Speech Acts10 Nov 2024