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Query-Based Abstractive Summarization Using Neural Networks
lib:2390c4132a5f7b8f (v1.0.0)
Authors: Johan Hasselqvist,Niklas Helmertz,Mikael Kågebäck
ArXiv: 1712.06100
Document: PDF DOI
Artifact development version: GitHub
Abstract URL: http://arxiv.org/abs/1712.06100v1
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.
ArXiv: 1712.06100
Document: PDF DOI
Artifact development version: GitHub
Abstract URL: http://arxiv.org/abs/1712.06100v1
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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