Generating Aspect-oriented Multi-Document Summarization with Event-aspect model

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.

• Type: Journal
• Year: 2011
• Where: EMNLP
• Authors: Peng Li, Yinglin Wang, Wei Gao, Jing Jiang
• Keywords: Compression Algorithm, Dependency Tree, EMNLP 2011, Natural Language Processing, Sentence Selection