Paper: Enhancing Single-Document Summarization by Combining RankNet and Third-Party Sources

ACL ID D07-1047
Title Enhancing Single-Document Summarization by Combining RankNet and Third-Party Sources
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2007
Authors

We present a new approach to automatic summarization based on neural nets, called NetSum. We extract a set of features from each sentence that helps identify its impor- tance in the document. We apply novel features based on news search query logs and Wikipedia entities. Using the RankNet learning algorithm, we train a pair-based sentence ranker to score every sentence in the document and identify the most impor- tant sentences. We apply our system to documents gathered from CNN.com, where each document includes highlights and an article. Our system significantly outper- forms the standard baseline in the ROUGE-1 measure on over 70% of our document set.