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SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization
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This paper introduces the SAMSum Corpus, a new dataset with abstractive dialogue summaries.We investigate the challenges it poses for automated summarization by testing several models and comparing their results with those obtained on a corpus of news articles.We show that model-generated summaries of dialogues achieve higher ROUGE scores than the model-generated summaries of news -in contrast with human evaluators' judgement.This suggests that a challenging task of abstractive dialogue summarization requires dedicated models and non-standard quality measures.To our knowledge, our study is the first attempt to introduce a high-quality chatdialogues corpus, manually annotated with abstractive summarizations, which can be used by the research community for further studies.
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“Previous research finds taking the longest sentence20 as dialogue summarization is a good baseline.”
From Aci-bench: a Novel Ambient Clinical Intelligence Dataset for Benchmarking Automatic Visit Note Generation (2023) · cites this paper as [20]
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Summ^N: A Multi-Stage Summarization Framework for Long Input Dialogues and DocumentsPreprint
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Does Pretraining for Summarization Require Knowledge Transfer?
Krishna, K., Bigham, J. P., & Lipton, Z. C. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.273 · Cited by 23
A Thorough Evaluation of Task-Specific Pretraining for Summarization
Rothe, S., Maynez, J., & Narayan, S. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.12 · Cited by 25
TWEETSUMM - A Dialog Summarization Dataset for Customer Service
Feigenblat, G., Gunasekara, C., Sznajder, B., Joshi, S., Konopnicki, D., & Aharonov, R. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.24 · Cited by 25
Pre-Training to Learn in Context
Gu, Y., Dong, L., Wei, F., & Huang, M. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.267 · Cited by 51
Molla, C., Mani, L., Bhuiyan, M. R. I., & Hossain, R. (2023) · MIGRATION LETTERS · doi:10.59670/ml.v20is9.4918 · Cited by 40
Baichuan2-Sum: Instruction Finetune Baichuan2-7B Model for Dialogue Summarization
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Can language representation models think in bets?
Tang, Z., & Kejriwal, M. (2023) · Royal Society Open Science · doi:10.1098/rsos.221585 · Cited by 23
Low-Resource Dialogue Summarization with Domain-Agnostic Multi-Source Pretraining
Zou, Y., Zhu, B., Hu, X., Gui, T., & Zhang, Q. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.7 · Cited by 22
An Analysis of Abstractive Text Summarization Using Pre-trained Models
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Bae, S., Ko, J., Song, H., & Yun, S.-Y. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.362 · Cited by 58
Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models
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Hierarchical Summarization for Longform Spoken Dialog
Li, D., Chen, T., Tung, A., & Chilton, L. B. (2021) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3472749.3474771 · Cited by 20
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Sparsing and Smoothing for the seq2seq Models
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Topic-Oriented Dialogue Summarization
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Dialogue Summarization with Static-Dynamic Structure Fusion Graph
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Hierarchical Speaker-Aware Sequence-to-Sequence Model for Dialogue Summarization
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DDRel: A New Dataset for Interpersonal Relation Classification in Dyadic Dialogues
Jia, Q., Huang, H., & Zhu, K. Q. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i14.17551 · Cited by 18
Using Question Answering Rewards to Improve Abstractive Summarization
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Metadata
- Authors
- Gliwa, B., Mochol, I., Biesek, M., & Wawer, A.
- Affiliations
- Samsung (Poland) (PL)
- Venue
- Proceedings of the 2nd Workshop on New Frontiers in Summarization
- Published
- 2019
- Pages
- 70-79
- OpenAlex
- W2989743967
- Cited by
- 424
- References
- 24
- Citations by year
- 2026: 30 · 2025: 72 · 2024: 52 · 2023: 125 · 2022: 72 · 2021: 67 · 2020: 6
- FWCI
- 13.57
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Workshop on New Frontiers in Summarization (NewSum) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
- Keywords
- abstractive summarization, dialogue summarization, news summarization
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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