Template-Based Headline Generator for Multiple Documents

Yun-Chien Tseng,Mu-Hua Yang,Wen-Chih Peng,Yao-Chung Fan,Chih-Chieh Hung

doi:10.1109/access.2022.3157287

Abstract

In this paper, we develop a neural multi-document summarization model, named MuD2H (refers to Multi-Document to Headline) to generate an attractive and customized headline from a set of product descriptions. To the best of our knowledge, no one has used a technique for multi-document summarization to generate headlines in the past. Therefore, multi-document headline generation can be considered new problem setting. Our model implements a two-stage architecture, including an extractive stage and an abstractive stage. The extractive stage is a graph-based model that identified salient sentences, whereas the abstractive stage uses existing summaries as soft templates to guild the seq2seq model. A series of experiments are conducted by using KKday dataset. Experimental results show that the proposed method outperforms the others in terms of quantitative and qualitative aspects.

Highlights

I N the era of information explosion, people eager to find a way to acquire knowledge efficiently
To quick capture the ideas behind articles, people are likely to go through headlines first and decide if an article is worthy to read
We aims at generating headlines for multiple documents

Summary

INTRODUCTION

I N the era of information explosion, people eager to find a way to acquire knowledge efficiently. TemPEST is a soft template-based seq2seq model [4], including three stages: Retrieve, Rerank and Rewrite. We proposed a model ”from MultiDocument to Headline”, which generates a personalized headline for a set of input documents. Instead of input user names and destinations in hard-template, our model is able to generate a real customized headline close to the user’s preference. The dataset we used includes product descriptions, product introductions and blog articles. Our contributions are summarized as follows: We propose a model MuD2H to generate a headline for a set of documents. This is the first work to use graph neural network to learn representative embed-.

Introduction

EXTRACTIVE SUMMARIZATION

EXPERIMENT

QUANTITATIVE RESULTS

Findings

CONCLUSION