Publication Date

Spring 2017

Degree Type

Master's Project

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Jenny Lam

Second Advisor

Chris Pollett

Third Advisor

Thomas Austin

Keywords

Headline Generation, Text Summarization, Neural Nets

Abstract

News headline generation is one of the important text summarization tasks. Human generated news headlines are generally intended to catch the eye rather than provide useful information. There have been many approaches to generate meaningful headlines by either using neural networks or using linguistic features. In this report, we are proposing a novel approach based on integrating Hedge Trimmer, which is a grammar based extractive summarization system with a deep neural network abstractive summarization system to generate meaningful headlines. We analyze the results against current recurrent neural network based headline generation system.

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