GLIMPSE: Pragmatically Informative Multi-Document Summarization for Scholarly Reviews (2024.acl-long)
Copied to clipboard
| Challenge: | Scientific peer review is essential for the quality of academic publications. |
| Approach: | They propose a method that summarises scholarly reviews using a Rational Speech Act framework and novel uniqueness scores. |
| Outcome: | The proposed method generates more discriminative summaries than baseline methods in terms of human evaluation while achieving comparable performance with these methods in term of automatic metrics. |
Similar Papers
A Critical Look at Meta-evaluating Summarisation Evaluation Metrics (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Effective summarisation evaluation metrics enable researchers and practitioners to compare different summarization systems efficiently. |
| Approach: | They argue that evaluation metrics are primarily meta-evaluated on news summarisation datasets and that there has been a noticeable shift in research focus towards evaluating the faithfulness of generated summaries. |
| Outcome: | The evaluation metrics are primarily meta-evaluated on news summarisation datasets and there has been a noticeable shift in research focus towards evaluating the faithfulness of generated summaries. |
Large-Scale and Multi-Perspective Opinion Summarization with Diverse Review Subsets (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for opinion summarization are deficient in epitomizing extensive reviews and offering opinion summaries from various angles. |
| Approach: | They propose a supervised opinion summarization framework that takes sentiment orientation into account and trains the summarizer to learn from sub-optimal and optimal review subsets. |
| Outcome: | The proposed framework generates pros, cons, and verdict summaries from hundreds of input reviews. |
Not All Reviews Are Equal: Towards Addressing Reviewer Biases for Opinion Summarization (P19-2)
Copied to clipboard
| Challenge: | Existing research focuses on mining for opinions from review texts and ignores reviewers. |
| Approach: | They propose to model reviewer biases from review texts and learn a bias-aware opinion representation. |
| Outcome: | The proposed method includes balanced opinions from reviewers with different biases and preferences. |
Fair Abstractive Summarization of Diverse Perspectives (2024.naacl-long)
Copied to clipboard
Yusen Zhang, Nan Zhang, Yixin Liu, Alexander Fabbri, Junru Liu, Ryo Kamoi, Xiaoxin Lu, Caiming Xiong, Jieyu Zhao, Dragomir Radev, Kathleen McKeown, Rui Zhang
| Challenge: | Existing work on summarization metrics and large language models has not explored fair abstractive summarizing. |
| Approach: | They propose four reference-free automatic metrics to measure the differences between target and source perspectives. |
| Outcome: | The proposed methods alleviate fair abstractive summarization on user-generated data. |
Rationale-based Opinion Summarization (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing methods to generate concise summaries of reviews are generic and lack supporting details. |
| Approach: | They propose a rationale-based opinion summarization paradigm that outputs representative opinions and corresponding rationales. |
| Outcome: | The proposed method is more useful than conventional summarizations. |
Attributable and Scalable Opinion Summarization (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for opinion summarization encode sentences from customer reviews into a hierarchical discrete latent space. |
| Approach: | They propose a method that encodes customer reviews into a hierarchical discrete latent space and then identifies common opinions based on their frequency. |
| Outcome: | The proposed method generates summaries that are more informative than previous work and more grounded in the input reviews. |
Summarizing Speech: A Comprehensive Survey (2025.emnlp-main)
Copied to clipboard
Fabian Retkowski, Maike Züfle, Andreas Sudmann, Dinah Pfau, Shinji Watanabe, Jan Niehues, Alexander Waibel
| Challenge: | Podcasts and other audiovisual content are becoming more and more a part of everyday communication and the digital age is changing from text to voice. |
| Approach: | They synthesize the current state of the field and highlight the need for realistic evaluation benchmarks and multilingual datasets. |
| Outcome: | The proposed frameworks are based on evaluation protocols and datasets and highlight the need for realistic benchmarks and multilingual datasets. |
Automated Metrics for Medical Multi-Document Summarization Disagree with Human Evaluations (2023.acl-long)
Copied to clipboard
Lucy Lu Wang, Yulia Otmakhova, Jay DeYoung, Thinh Hung Truong, Bailey Kuehl, Erin Bransom, Byron Wallace
| Challenge: | Prior work has shown that models may exploit shortcuts that are difficult to detect using standard n-gram similarity metrics such as ROUGE. |
| Approach: | They propose to use human-assessed summary quality facets and pairwise preferences to improve MDS evaluation methods. |
| Outcome: | The proposed methods improve the quality of literature review summarization models . they use human-assessed summary quality facets and pairwise preferences . |
Bringing Structure into Summaries: a Faceted Summarization Dataset for Long Scientific Documents (2021.acl-short)
Copied to clipboard
| Challenge: | Faceted summarization provides briefings of a document from different perspectives. |
| Approach: | They propose a faceted summarization benchmark built on Emerald journal articles . they propose faceted models that bring structure into faceted documents . |
| Outcome: | The proposed benchmark is based on Emerald journal articles and covers a diverse range of domains. |
Summarizing Multiple Documents with Conversational Structure for Meta-Review Generation (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing models for abstractive text summarization do not provide explicit interdocument relationships among source documents. |
| Approach: | They propose a model that uses sparse attention based on the conversational structure and a multi-task training objective that predicts metadata features. |
| Outcome: | The proposed model outperforms baseline models in terms of evaluation metrics but struggle to handle conflicts in source documents. |