Query-Based Abstractive Summarization Using Neural Networks

Abstract

In this paper, we present a model for generating summaries of text documents with respect to a query. This is known as query-based summarization. We adapt an existing dataset of news article summaries for the task and train a pointer-generator model using this dataset. The generated summaries are evaluated by measuring similarity to reference summaries. Our results show that a neural network summarization model, similar to existing neural network models for abstractive summarization, can be constructed to make use of queries to produce targeted summaries.

References in corpus (4)

• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
• A Sentence Compression Based Framework to Query-Focused Multi-Document Summarization
• Temporal Attention Model for Neural Machine Translation