Using Question Answering Rewards to Improve Abstractive Summarization
Chulaka Gunasekara, Guy Feigenblat, Benjamin Sznajder, Ranit Aharonov, Sachindra Joshi · 2021
Neural abstractive summarization models have drastically improved in the recent years.However, the summaries generated by these models generally suffer from issues such as: not capturing the critical facts in source documents, and containing facts that are inconsistent with the source documents.In this work, we present a general framework to train abstractive summarization models to alleviate such issues.We first train a sequence-to-sequence model to summarize documents, and then further train this model in a Reinforcement Learning setting with question-answering based rewards.We evaluate the summaries generated by the this framework using multiple automatic measures and human judgements.The experimental results show that the question-answering rewards can be used as a general framework to improve neural abstractive summarization.Particularly, the results from human evaluations show that the summaries generated by our approach are preferred over 30% of the time over the summaries generated by general abstractive summarization models.Original Document/Dialog Charlee: I'm