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Radical Enhanced Chinese Word Embedding Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Review Headline Generation with User Embedding

Authors : Tianshang Liu, Haoran Li, Junnan Zhu, Jiajun Zhang, Chengqing Zong

Published in: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data

Publisher: Springer International Publishing

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Abstract

In this paper, we conduct a review headline generation task that produces a short headline from a review post by a user. We argue that this task is more challenging than document summarization, because the headlines generated by users vary from person to person. It not only needs to effectively capture the preferences of the users who post the reviews, but also requires to mine the emphasis of the users regarding the review when they write the headlines. To this end, we propose to incorporate the user information as the prior knowledge into the encoder and decoder for general sequence-to-sequence model. Specifically, we introduce user embedding for each user, and then we use these embeddings to initialize the encoder and decoder, or as biases for decoder initialization. We construct a review headline generation dataset, and the experiments on this dataset demonstrate that our models significantly outperform baseline models which do not consider user information.

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previous chapter A Word Embedding Transfer Model for Robust Text Categorization
next chapter A Joint Model for Sentiment Classification and Opinion Words Extraction
Footnotes
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Metadata
Title
Review Headline Generation with User Embedding
Authors
Tianshang Liu
Haoran Li
Junnan Zhu
Jiajun Zhang
Chengqing Zong
Copyright Year
2018
Book
Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data

Print ISBN: 978-3-030-01715-6

Electronic ISBN: 978-3-030-01716-3

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-01716-3_27

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