Query-Focused Abstractive Summarization using Neural Networks

Abstract

Query-focused abstractive document summarization (QFADS) is a process of shortening a document into a summary while keeping the context of query in mind. We implemented a model consisting of a novel selective mechanism for QFADS. A selective mechanism was used for improving the representation of a long input (passage) sequence. We conducted experiments on the Debatepedia dataset, a recently developed dataset for query-focused abstractive summarization task, which showed that our model outperforms the state-of-the-art model in all ROUGE scores. Also, we proposed three models all of which consist of a coarse-to-fine approach and a novel selective mechanism for query-focused abstractive multi document summarization (QFAMDS). The coarse-to-fine approach was used to reduce the length of the passage input from multiple documents. We conducted experiments on the MS MARCO dataset, a recently developed large scale dataset by Microsoft for reading comprehension, and have reported our scores using various evaluation metrics.