  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.
