Neural Extractive Summarization with Side Information

2017/04/14 by Shashi Narayan, Narayan, Shashi, Nikos Papasarantopoulos +5 · 1 citation
Computer Science · #Advanced Text Analysis Techniques #Computation and Language (cs.CL) #FOS: Computer and information sciences #Natural Language Processing Techniques #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1704.04530

openalex publication_date 2017/04/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Most extractive summarization methods focus on the main body of the document from which sentences need to be extracted. However, the gist of the document may lie in side information, such as the title and image captions which are often available for newswire articles. We propose to explore side information in the context of single-document extractive summarization. We develop a framework for single-document summarization composed of a hierarchical document encoder and an attention-based extractor with attention over side information. We evaluate our model on a large scale news dataset. We show that extractive summarization with side information consistently outperforms its counterpart that does not use any side information, in terms of both informativeness and fluency.

Citations

• Teaching Machines to Read and Comprehend
• Character-Aware Neural Language Models
• Long Short-Term Memory
• Sequence to Sequence Learning with Neural Networks

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