Generating Aspect-oriented Multi-Document Summarization with Event-aspect model.
EMNLP '11: Proceedings of the Conference on Empirical Methods in Natural Language Processing(2011)
摘要
In this paper, we propose a novel approach to automatic generation of aspect-oriented summaries from multiple documents. We first develop an event-aspect LDA model to cluster sentences into aspects. We then use extended LexRank algorithm to rank the sentences in each cluster. We use Integer Linear Programming for sentence selection. Key features of our method include automatic grouping of semantically related sentences and sentence ranking based on extension of random walk model. Also, we implement a new sentence compression algorithm which use dependency tree instead of parser tree. We compare our method with four baseline methods. Quantitative evaluation based on Rouge metric demonstrates the effectiveness and advantages of our method.
更多查看译文
关键词
baseline method,cluster sentence,new sentence compression algorithm,semantically related sentence,sentence selection,LexRank algorithm,automatic generation,automatic grouping,dependency tree,event-aspect LDA model,event-aspect model,multi-document summarization
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络