Comparison of Data Augmentation Methods in Pointer-Generator Model Using Various Sentence Ranking Methods

被引：0

作者：

Ouchi, Tomohito ^{[1
]}

Tabuse, Masayoshi ^{[1
]}

机构：

[1] Kyoto Prefectural Univ, Grad Sch Life & Environm Sci, Sakyo Ku, 1-5 Shimogamohangi Cho, Kyoto 6068522, Japan

来源：

PROCEEDINGS OF THE 2021 INTERNATIONAL CONFERENCE ON ARTIFICIAL LIFE AND ROBOTICS (ICAROB 2021) | 2021年

关键词：

automatic summarization; data augmentation; Pointer-Generator Model; Extractive summarization;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In the existing research, we proposed a data augmentation method using topic model for Pointer-Generator model. In this study, we add to the sentence ranking method in the data augmentation method. Specifically, we add two ranking methods using LexRank and Luhn. LexRank is based on Google's search method and Luhn defines sentence features and ranks sentences. We compare three data augmentation methods. We considered which method is suitable for data augmentation. We confirm that most accurate model is the model using data augmentation method by topic model.

引用

页码：20 / 23

页数：4

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