SRRank: Leveraging Semantic Roles for Extractive Multi-Document Summarization

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.

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