SummN: A Multi-Stage Summarization Framework for Long Input Dialogues and Documents: A Multi-Stage Summarization Framework for Long Input Dialogues and Documents
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Yusen Zhang, Ansong Ni, Ziming Mao, Chen Henry Wu, Chenguang Zhu, Budhaditya Deb, Ahmed Awadallah, Dragomir Radev, Rui Zhang. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.
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Prior research has effectively leveraged context compression techniques such as retrieval and truncation to address long text tasks with pretrained language models.
“Prior research has effectively leveraged context compression techniques such as retrieval and truncation to address long text tasks with pretrained language models (Gong et al., 2020; Zhang et al., 2022; Zhao et al., 2022).”
From BAMBOO: A Comprehensive Benchmark for Evaluating Long Text Modeling Capacities of Large Language Models (2023) · cites this paper as Zhang et al., 2022
The Introduction (page unavailable in our copy) identifies truncation and retrieve-then-summarize pipelines as common ways to shorten long inputs for language models. It also says these methods break contextual dependencies and reduce the text the model can read, so the claim that they were leveraged *effectively* overstates what this source supports. (LLM, conf=0.84)
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- Authors
- Zhang, Y., Ni, A., Mao, Z., Wu, C. H., Zhu, C., Deb, B., Awadallah, A., Radev, D., & Zhang, R.
- Affiliations
- Pennsylvania State University (US)
- Yale University (US)
- Microsoft (United States) (US)
- Venue
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2022
- Pages
- 1592-1604
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W4285299836
- Cited by
- 44
- References
- 41
- Citations by year
- 2026: 5 · 2025: 6 · 2024: 7 · 2023: 24 · 2022: 2
- FWCI
- 5.07
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- Funders
- Microsoft Research
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- long documents, dialogue summarization, abstractive summarization, text 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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