Topic-Guided Self-Introduction Generation for Social Media Users
Chunpu Xu Jing Li Piji Li Min Yang
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 profiles 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.