@inproceedings{misawa-etal-2020-distinctive,
title = "Distinctive Slogan Generation with Reconstruction",
author = "Misawa, Shotaro and
Miura, Yasuhide and
Taniguchi, Tomoki and
Ohkuma, Tomoko",
editor = "Zhao, Huasha and
Sondhi, Parikshit and
Bach, Nguyen and
Hewavitharana, Sanjika and
He, Yifan and
Si, Luo and
Ji, Heng",
booktitle = "Proceedings of Workshop on Natural Language Processing in E-Commerce",
month = dec,
year = "2020",
address = "Barcelona, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.ecomnlp-1.9",
pages = "87--97",
abstract = "E-commerce sites include advertising slogans along with information regarding an item. Slogans can attract viewers{'} attention to increase sales or visits by emphasizing advantages of an item. The aim of this study is to generate a slogan from a description of an item. To generate a slogan, we apply an encoder{--}decoder model which has shown effectiveness in many kinds of natural language generation tasks, such as abstractive summarization. However, slogan generation task has three characteristics that distinguish it from other natural language generation tasks: distinctiveness, topic emphasis, and style difference. To handle these three characteristics, we propose a compressed representation{--}based reconstruction model with refer{--}attention and conversion layers. The results of the experiments indicate that, based on automatic and human evaluation, our method achieves higher performance than conventional methods.",
}
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<abstract>E-commerce sites include advertising slogans along with information regarding an item. Slogans can attract viewers’ attention to increase sales or visits by emphasizing advantages of an item. The aim of this study is to generate a slogan from a description of an item. To generate a slogan, we apply an encoder–decoder model which has shown effectiveness in many kinds of natural language generation tasks, such as abstractive summarization. However, slogan generation task has three characteristics that distinguish it from other natural language generation tasks: distinctiveness, topic emphasis, and style difference. To handle these three characteristics, we propose a compressed representation–based reconstruction model with refer–attention and conversion layers. The results of the experiments indicate that, based on automatic and human evaluation, our method achieves higher performance than conventional methods.</abstract>
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%0 Conference Proceedings
%T Distinctive Slogan Generation with Reconstruction
%A Misawa, Shotaro
%A Miura, Yasuhide
%A Taniguchi, Tomoki
%A Ohkuma, Tomoko
%Y Zhao, Huasha
%Y Sondhi, Parikshit
%Y Bach, Nguyen
%Y Hewavitharana, Sanjika
%Y He, Yifan
%Y Si, Luo
%Y Ji, Heng
%S Proceedings of Workshop on Natural Language Processing in E-Commerce
%D 2020
%8 December
%I Association for Computational Linguistics
%C Barcelona, Spain
%F misawa-etal-2020-distinctive
%X E-commerce sites include advertising slogans along with information regarding an item. Slogans can attract viewers’ attention to increase sales or visits by emphasizing advantages of an item. The aim of this study is to generate a slogan from a description of an item. To generate a slogan, we apply an encoder–decoder model which has shown effectiveness in many kinds of natural language generation tasks, such as abstractive summarization. However, slogan generation task has three characteristics that distinguish it from other natural language generation tasks: distinctiveness, topic emphasis, and style difference. To handle these three characteristics, we propose a compressed representation–based reconstruction model with refer–attention and conversion layers. The results of the experiments indicate that, based on automatic and human evaluation, our method achieves higher performance than conventional methods.
%U https://aclanthology.org/2020.ecomnlp-1.9
%P 87-97
Markdown (Informal)
[Distinctive Slogan Generation with Reconstruction](https://aclanthology.org/2020.ecomnlp-1.9) (Misawa et al., EcomNLP 2020)
ACL
- Shotaro Misawa, Yasuhide Miura, Tomoki Taniguchi, and Tomoko Ohkuma. 2020. Distinctive Slogan Generation with Reconstruction. In Proceedings of Workshop on Natural Language Processing in E-Commerce, pages 87–97, Barcelona, Spain. Association for Computational Linguistics.