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.

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Cited by in corpus (8)

• AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization
• Conditional Self-Attention for Query-based Summarization
• Multi-style Generative Reading Comprehension
• Meaningful Answer Generation of E-Commerce Question-Answering
• Multi-hop Inference for Question-driven Summarization
• Product-Aware Answer Generation in E-Commerce Question-Answering
• Hone as You Read: A Practical Type of Interactive Summarization
• Query Understanding via Intent Description Generation