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        <title>Topic-Guided Self-Introduction Generation for Social Media Users</title>
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        <namePart type="given">Chunpu</namePart>
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        <namePart type="given">Jing</namePart>
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    <name type="personal">
        <namePart type="given">Min</namePart>
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    <abstract>Millions of users are active on social media. To allow users to better showcase themselves and network with others, we explore the auto-generation of social media self-introduction, a short sentence outlining a user‘s personal interests. While most prior work profiling users with tags (e.g., ages), we investigate sentence-level self-introductions to provide a more natural and engaging way for users to know each other. Here we exploit a user‘s tweeting history to generate their self-introduction. The task is non-trivial because the history content may be lengthy, noisy, and exhibit various personal interests. To address this challenge, we propose a novel unified topic-guided encoder-decoder (UTGED) framework; it models latent topics to reflect salient user interest, whose topic mixture then guides encoding a user‘s history and topic words control decoding their self-introduction. For experiments, we collect a large-scale Twitter dataset, and extensive results show the superiority of our UTGED to the advanced encoder-decoder models without topic modeling.</abstract>
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%0 Conference Proceedings
%T Topic-Guided Self-Introduction Generation for Social Media Users
%A Xu, Chunpu
%A Li, Jing
%A Li, Piji
%A Yang, Min
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F xu-etal-2023-topic
%X Millions of users are active on social media. To allow users to better showcase themselves and network with others, we explore the auto-generation of social media self-introduction, a short sentence outlining a user‘s personal interests. While most prior work profiling users with tags (e.g., ages), we investigate sentence-level self-introductions to provide a more natural and engaging way for users to know each other. Here we exploit a user‘s tweeting history to generate their self-introduction. The task is non-trivial because the history content may be lengthy, noisy, and exhibit various personal interests. To address this challenge, we propose a novel unified topic-guided encoder-decoder (UTGED) framework; it models latent topics to reflect salient user interest, whose topic mixture then guides encoding a user‘s history and topic words control decoding their self-introduction. For experiments, we collect a large-scale Twitter dataset, and extensive results show the superiority of our UTGED to the advanced encoder-decoder models without topic modeling.
%R 10.18653/v1/2023.findings-acl.722
%U https://aclanthology.org/2023.findings-acl.722/
%U https://doi.org/10.18653/v1/2023.findings-acl.722
%P 11387-11402

Markdown (Informal)

[Topic-Guided Self-Introduction Generation for Social Media Users](https://aclanthology.org/2023.findings-acl.722/) (Xu et al., Findings 2023)

• Topic-Guided Self-Introduction Generation for Social Media Users (Xu et al., Findings 2023)

ACL

• Chunpu Xu, Jing Li, Piji Li, and Min Yang. 2023. Topic-Guided Self-Introduction Generation for Social Media Users. In Findings of the Association for Computational Linguistics: ACL 2023, pages 11387–11402, Toronto, Canada. Association for Computational Linguistics.