FastSum: Fast and Accurate Query-based Multi-document Summarization

We present a fast query-based multi-document summarizer called FastSum based solely on word-frequency features of clusters, documents and topics. Summary sentences are ranked by a regression SVM. The summarizer does not use any expensive NLP techniques such as parsing, tagging of names or even part of speech information. Still, the achieved accuracy is comparable to the best systems presented in recent academic competitions (i.e., Document Understanding Conference (DUC)). Because of a detailed feature analysis using Least Angle Regression (LARS), FastSum can rely on a minimal set of features leading to fast processing times: 1250 news documents in 60 seconds.

• Type: Conference
• Year: 2008
• Where: ACL
• Authors: Frank Schilder, Ravikumar Kondadadi
• Keywords: ACL 2008, Computational Linguistics, Least Angle Regression, Regression Svm, Summarizer Called Fastsum