A Survey on Neural Network-Based Summarization Methods

Abstract

Automatic text summarization, the automated process of shortening a text while reserving the main ideas of the document(s), is a critical research area in natural language processing. The aim of this literature review is to survey the recent work on neural-based models in automatic text summarization. We examine in detail ten state-of-the-art neural-based summarizers: five abstractive models and five extractive models. In addition, we discuss the related techniques that can be applied to the summarization tasks and present promising paths for future research in neural-based summarization.

16 pages, 4 tables

References in corpus (3)

• A Deep Reinforced Model for Abstractive Summarization
• SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents
• An Actor-Critic Algorithm for Sequence Prediction

Cited by in corpus (4)

• Abstractive Summarization Using Attentive Neural Techniques
• Experiments in Extractive Summarization: Integer Linear Programming, Term/Sentence Scoring, and Title-driven Models
• From web crawled text to project descriptions: automatic summarizing of social innovation projects
• GASP! Generating Abstracts of Scientific Papers from Abstracts of Cited Papers