Cross-Document Event-Keyed Summarization
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913613051068416 |
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| author | Walden, William Kuchmiichuk, Pavlo Martin, Alexander Jin, Chihsheng Cao, Angela Sun, Claire Allen, Curisia White, Aaron Steven |
| author_facet | Walden, William Kuchmiichuk, Pavlo Martin, Alexander Jin, Chihsheng Cao, Angela Sun, Claire Allen, Curisia White, Aaron Steven |
| contents | Event-keyed summarization (EKS) requires summarizing a specific event described in a document given the document text and an event representation extracted from it. In this work, we extend EKS to the cross-document setting (CDEKS), in which summaries must synthesize information from accounts of the same event as given by multiple sources. We introduce SEAMUS (Summaries of Events Across Multiple Sources), a high-quality dataset for CDEKS based on an expert reannotation of the FAMUS dataset for cross-document argument extraction. We present a suite of baselines on SEAMUS -- covering both smaller, fine-tuned models, as well as zero- and few-shot prompted LLMs -- along with detailed ablations and a human evaluation study, showing SEAMUS to be a valuable benchmark for this new task. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_14795 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Cross-Document Event-Keyed Summarization Walden, William Kuchmiichuk, Pavlo Martin, Alexander Jin, Chihsheng Cao, Angela Sun, Claire Allen, Curisia White, Aaron Steven Computation and Language Event-keyed summarization (EKS) requires summarizing a specific event described in a document given the document text and an event representation extracted from it. In this work, we extend EKS to the cross-document setting (CDEKS), in which summaries must synthesize information from accounts of the same event as given by multiple sources. We introduce SEAMUS (Summaries of Events Across Multiple Sources), a high-quality dataset for CDEKS based on an expert reannotation of the FAMUS dataset for cross-document argument extraction. We present a suite of baselines on SEAMUS -- covering both smaller, fine-tuned models, as well as zero- and few-shot prompted LLMs -- along with detailed ablations and a human evaluation study, showing SEAMUS to be a valuable benchmark for this new task. |
| title | Cross-Document Event-Keyed Summarization |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2410.14795 |