Paper: Fear the REAPER: A System for Automatic Multi-Document Summarization with Reinforcement Learning

ACL ID D14-1075
Title Fear the REAPER: A System for Automatic Multi-Document Summarization with Reinforcement Learning
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

This paper explores alternate algorithms, reward functions and feature sets for per- forming multi-document summarization using reinforcement learning with a high focus on reproducibility. We show that ROUGE results can be improved using a unigram and bigram similarity metric when training a learner to select sentences for summarization. Learners are trained to summarize document clusters based on various algorithms and reward functions and then evaluated using ROUGE. Our ex- periments show a statistically significant improvement of 1.33%, 1.58%, and 2.25% for ROUGE-1, ROUGE-2 and ROUGE- L scores, respectively, when compared with the performance of the state of the art in automatic summarization with re- inforcement learning on the DUC2004 dataset. Furthermore query focused exten- sions of...