A Sentence Compression Based Framework to Query-Focused Multi-Document Summarization
We consider the problem of using sentence compression techniques to facilitate query-focused multi-document summarization. We present a sentence-compression-based framework for the task, and design a series of learning-based compression models built on parse trees. An innovative beam search decoder is proposed to efficiently find highly probable compressions. Under this framework, we show how to integrate various indicative metrics such as linguistic motivation and query relevance into the compression process by deriving a novel formulation of a compression scoring function. Our best model achieves statistically significant improvement over the state-of-the-art systems on several metrics (e.g. 8.0% and 5.4% improvements in ROUGE-2 respectively) for the DUC 2006 and 2007 summarization task.
Code (0)
등록된 구현이 없습니다.
Tasks
DecoderDocument SummarizationMulti-Document SummarizationSentenceSentence CompressionSimilar Papers 제목 키워드 기반
Query-focused Sentence Compression in Linear Time
Search applications often display shortened sentences which must contain certain query terms and must fit within the space constraints of a user interface. This work introduces a new transition-based sentence compression…
GPUSentenceSentence CompressionQuery-focused Sentence Compression in Linear Time
Search applications often display shortened sentences which must contain certain query terms and must fit within the space constraints of a user interface. This work introduces a new transition-based sentence compression…
GPUSentenceSentence CompressionTowards Abstractive Multi-Document Summarization Using Submodular Function-Based Framework, Sentence Compression and Merging
We propose a submodular function-based summarization system which integrates three important measures namely importance, coverage, and non-redundancy to detect the important sentences for the summary. We design monotone …
Abstractive Text SummarizationDocument SummarizationMulti-Document SummarizationQuery-focused Summarization+3On the Effectiveness of using Sentence Compression Models for Query-Focused Multi-Document Summarization
Query Focused Multi-document Summarisation of Biomedical Texts
This paper presents the participation of Macquarie University and the Australian National University for Task B Phase B of the 2020 BioASQ Challenge (BioASQ8b). Our overall framework implements Query focused multi-docume…
regressionreinforcement-learningReinforcement Learning (RL)Sentence+2