# GLIMPSE: Pragmatically Informative Multi-Document Summarization for Scholarly Reviews

arXiv: https://arxiv.org/abs/2406.07359 · HTML: https://mlsanity.com/paper/2406.07359/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 43 references that are in the map.
- First posted on arXiv 11 Jun 2024.

## Builds on (most cited first, 21 of 21)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [Evaluating the Factual Consistency of Abstractive Text Summarization](https://mlsanity.com/paper/1910.12840/) (2019)
- [To Ship or Not to Ship: An Extensive Evaluation of Automatic Metrics for Machine Translation](https://mlsanity.com/paper/2107.10821/) (2021)
- [Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised](https://mlsanity.com/paper/1808.08858/) (2018)
- [Unsupervised Opinion Summarization as Copycat-Review Generation](https://mlsanity.com/paper/1911.02247/) (2019)
- [Aspect-Controllable Opinion Summarization](https://mlsanity.com/paper/2109.03171/) (2021)
- [Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes](https://mlsanity.com/paper/2109.08828/) (2021)
- [Pragmatically Informative Image Captioning with Character-Level Inference](https://mlsanity.com/paper/1804.05417/) (2018)
- [SEAHORSE: A Multilingual, Multifaceted Dataset for Summarization Evaluation](https://mlsanity.com/paper/2305.13194/) (2023)
- [OpinionDigest: A Simple Framework for Opinion Summarization](https://mlsanity.com/paper/2005.01901/) (2020)
- [Pragmatically Informative Text Generation](https://mlsanity.com/paper/1904.01301/) (2019)
- [Informative and Controllable Opinion Summarization](https://mlsanity.com/paper/1909.02322/) (2019)
- [Unsupervised Opinion Summarization with Content Planning](https://mlsanity.com/paper/2012.07808/) (2020)
- [A Rate-Distortion view of human pragmatic reasoning](https://mlsanity.com/paper/2005.06641/) (2020)
- [Scientific Opinion Summarization: Paper Meta-review Generation Dataset, Methods, and Evaluation](https://mlsanity.com/paper/2305.14647/) (2023)
- [Lost in Machine Translation: A Method to Reduce Meaning Loss](https://mlsanity.com/paper/1902.09514/) (2019)
- [Weakly-Supervised Opinion Summarization by Leveraging External Information](https://mlsanity.com/paper/1911.09844/) (2019)
- [Calibrate your listeners! Robust communication-based training for pragmatic speakers](https://mlsanity.com/paper/2110.05422/) (2021)
- [On Rate-Distortion Theory in Capacity-Limited Cognition & Reinforcement Learning](https://mlsanity.com/paper/2210.16877/) (2022)

## Built on by (most cited first, 6 of 6)

- [From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems](https://mlsanity.com/paper/2503.01424/) (2025)
- [DSAS: A Universal Plug-and-Play Framework for Attention Optimization in Multi-Document Question Answering](https://mlsanity.com/paper/2510.12251/) (2025)
- [Collaborative Rational Speech Act: Pragmatic Reasoning for Multi-Turn Dialog](https://mlsanity.com/paper/2507.14063/) (2025)
- [When AI reviews science: Can we trust the referee?](https://mlsanity.com/paper/2604.23593/) (2026)
- [Does This Summary Answer My Question? Modeling Query-Focused Summary Readers with Rational Speech Acts](https://mlsanity.com/paper/2411.06524/) (2024)
- [MVSS: A Unified Framework for Multi-View Structured Survey Generation](https://mlsanity.com/paper/2601.09504/) (2026)

## 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.