  Hashtag generation aims to generate short and informal topical tags from a
microblog post, in which tokens or phrases form the hashtags. These tokens or
phrases may originate from primary fragmental textual pieces (e.g., segments)
in the original text and are separated into different segments. However,
conventional sequence-to-sequence generation methods are hard to filter out
secondary information from different textual granularity and are not good at
selecting crucial tokens. Thus, they are suboptimal in generating more
condensed hashtags. In this work, we propose a modified Transformer-based
generation model with adding a segments-selection procedure for the original
encoding and decoding phases. The segments-selection phase is based on a novel
Segments Selection Mechanism (SSM) to model different textual granularity on
global text, local segments, and tokens, contributing to generating condensed
hashtags. Specifically, it first attends to primary semantic segments and then
transforms discontinuous segments from the source text into a sequence of
hashtags by selecting crucial tokens. Extensive evaluations on the two datasets
reveal our approach's superiority with significant improvements to the
extraction and generation baselines. The code and datasets are available at
https://github.com/OpenSUM/HashtagGen.
