Paper: Keyphrase Extraction for N-best Reranking in Multi-Sentence Compression

ACL ID N13-1030
Title Keyphrase Extraction for N-best Reranking in Multi-Sentence Compression
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2013
Authors

Multi-Sentence Compression (MSC) is the task of generating a short single sentence sum- mary from a cluster of related sentences. This paper presents an N-best reranking method based on keyphrase extraction. Compression candidates generated by a word graph-based MSC approach are reranked according to the number and relevance of keyphrases they con- tain. Both manual and automatic evaluations were performed using a dataset made of clus- ters of newswire sentences. Results show that the proposed method significantly improves the informativity of the generated compres- sions.