Query-based summarization using reinforcement learning and transformer model
University of Lethbridge. Faculty of Arts and Science
Lethbridge, Alta. : University of Lethbridge, Dept. of Mathematics and Computer Science
Query-based summarization problem is an interesting problem in the text summarization field. On the other hand, the reinforcement learning technique is popular for robotics and becoming accessible for the text summarization problem in the last couple of years (Narayan et al., 2018). The lack of significant works using reinforcement learning to solve the query-based summarization problem inspired us to use this technique. While doing so, We also introduce a different approach for sentence ranking and clustering to avoid redundancy in summaries. We propose an unsupervised extractive summarization method, which provides state-of-the-art results on some metrics. We develop two abstractive multi-document summarization models using the reinforcement learning technique and the transformer model (Vaswani et al., 2017). We consider the importance of information coverage and diversity under a fixed sentence limit for our summarization models. We have done several experiments for our proposed models, which bring significant results across different evaluation metrics.
Software engineering , Systems engineering , Text data mining , Natural language processing , Natural language generation (Computer science)