SRRank: Leveraging Semantic Roles for Extractive Multi-Document Summarization | |
Yan, Su ; Wan, Xiaojun | |
刊名 | ieee acm transactions on audio speech and language processing |
2014 | |
关键词 | Graph-based ranking algorithm multi-document summarization semantic roles |
DOI | 10.1109/TASLP.2014.2360461 |
英文摘要 | Extractive multi-document summarization systems usually rank sentences in a document set with some ranking strategy and then select a few highly ranked sentences into the summary. One of the most popular ranking algorithms is the graph-based ranking algorithm. In this paper, we investigate making use of semantic role information to enhance the graph-based ranking algorithm for multi-document summarization. We first parse the sentences and obtain the semantic roles, and then propose a novel SRRank algorithm and two extensions to make better use of the semantic role information. Our proposed algorithms can simultaneously rank the sentences, semantic roles and words in a heterogeneous ranking process. Experimental results on two DUC datasets demonstrate that our proposed algorithms significantly outperform a few baselines, and the semantic role information is validated to be very helpful for multi-document summarization.; Acoustics; Engineering, Electrical & Electronic; SCI(E); EI; 0; ARTICLE; yansu@pku.edu.cn; wanxiaojun@pku.edu.cn; 12; 2048-2058; 22 |
语种 | 英语 |
内容类型 | 期刊论文 |
源URL | [http://ir.pku.edu.cn/handle/20.500.11897/161732] |
专题 | 信息科学技术学院 |
推荐引用方式 GB/T 7714 | Yan, Su,Wan, Xiaojun. SRRank: Leveraging Semantic Roles for Extractive Multi-Document Summarization[J]. ieee acm transactions on audio speech and language processing,2014. |
APA | Yan, Su,&Wan, Xiaojun.(2014).SRRank: Leveraging Semantic Roles for Extractive Multi-Document Summarization.ieee acm transactions on audio speech and language processing. |
MLA | Yan, Su,et al."SRRank: Leveraging Semantic Roles for Extractive Multi-Document Summarization".ieee acm transactions on audio speech and language processing (2014). |
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