Integrating Rhetorical-Semantic Relation Models for Query-Focused Summarization

McKeown, Kathleen; Blair-Goldensohn, Sasha

doi:10.7916/d8j96fth

2006

DOI: 10.7916/d8j96fth

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Integrating Rhetorical-Semantic Relation Models for Query-Focused Summarization

Kathleen McKeown¹,

Sasha Blair-Goldensohn²

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Cited by 3 publications

References 11 publications

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Modeling Preconditions in Text with a Crowd-sourced Dataset

Kwon

Koupaee

Singh³

et al. 2020

Findings of the Association for Computational Linguistics: EMNLP 2020

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Preconditions provide a form of logical connection between events that explains why some events occur together and information that is complementary to the more widely studied relations such as causation, temporal ordering, entailment, and discourse relations. Modeling preconditions in text has been hampered in part due to the lack of large scale labeled data grounded in text. This paper introduces PeKo, a crowd-sourced annotation of preconditions between event pairs in newswire, an order of magnitude larger than prior text annotations. To complement this new corpus, we also introduce two challenge tasks aimed at modeling preconditions: (i) Precondition Identification -a standard classification task defined over pairs of event mentions, and (ii) Precondition Generation -a generative task aimed at testing a more general ability to reason about a given event. Evaluation on both tasks shows that modeling preconditions is challenging even for today's large language models (LM). This suggests that precondition knowledge is not easily accessible in LM-derived representations alone. Our generation results show that fine-tuning an LM on PeKo yields better conditional relations than when trained on raw text or temporally-ordered corpora.

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Modeling Preconditions in Text with a Crowd-sourced Dataset

Kwon

Koupaee

Singh³

et al. 2020

Findings of the Association for Computational Linguistics: EMNLP 2020

View full text Add to dashboard Cite

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Discourse oriented summarization

Bosma¹

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Question Answering using Integrated Information Retrieval and Information Extraction

Schiffman

McKeown

Grishman³

et al. 2007

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Integrating Rhetorical-Semantic Relation Models for Query-Focused Summarization

Cited by 3 publications

References 11 publications

Modeling Preconditions in Text with a Crowd-sourced Dataset

Modeling Preconditions in Text with a Crowd-sourced Dataset

Discourse oriented summarization

Question Answering using Integrated Information Retrieval and Information Extraction

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