FAR-ASS: Fact-aware reinforced abstractive sentence summarization

Abstract

Automatic summarization systems provide an effective solution to today's unprecedented growth of textual data. For real-world tasks, such as data mining and information retrieval, the factual correctness of generated summary is critical. However, existing models usually focus on improving the informativeness rather than optimizing factual correctness. In this work, we present a Fact-Aware Reinforced Abstractive Sentence Summarization framework to improve the factual correctness of neural abstractive summarization models, denoted as FAR-ASS. Specifically, we develop an automatic fact extraction scheme leveraging OpenIE (Open Information Extraction) and dependency parser tools to extract structured fact tuples. Then, to quantitatively evaluate the factual correctness, we define a factual correctness score function that considers the factual accuracy and factual redundancy. We further propose to adopt reinforcement learning to improve readability and factual correctness by jointly optimizing a mixed-objective learning function. We use the English Gigaword and DUC 2004 datasets to evaluate our model. Experimental results show that compared with competitive models, our model significantly improves the factual correctness and readability of generated summaries, and also reduces duplicates while improving the informativeness.