Paper: Improving Multi-documents Summarization by Sentence Compression based on Expanded Constituent Parse Trees

ACL ID D14-1076
Title Improving Multi-documents Summarization by Sentence Compression based on Expanded Constituent Parse Trees
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

In this paper, we focus on the problem of using sentence compression techniques to improve multi-document summariza- tion. We propose an innovative sentence compression method by considering every node in the constituent parse tree and de- ciding its status ? remove or retain. In- teger liner programming with discrimina- tive training is used to solve the problem. Under this model, we incorporate various constraints to improve the linguistic qual- ity of the compressed sentences. Then we utilize a pipeline summarization frame- work where sentences are first compressed by our proposed compression model to ob- tain top-n candidates and then a sentence selection module is used to generate the final summary. Compared with state-of- the-art algorithms, our model has simi- lar ROUGE-2 scores but...