TL;DR
This paper introduces a segment selective transformer with a novel segments selection mechanism to improve the quality and condensation of hashtags generated from microblog posts, outperforming existing methods.
Contribution
It proposes a new transformer model with a segments-selection procedure that effectively filters crucial tokens for better hashtag generation.
Findings
Significant improvements over baselines in evaluation metrics.
Effective modeling of different textual granularities.
Enhanced ability to generate condensed, relevant hashtags.
Abstract
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…
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Taxonomy
MethodsMulti-Head Attention · Attention Is All You Need · Linear Layer · Absolute Position Encodings · Position-Wise Feed-Forward Layer · Byte Pair Encoding · Adam · Label Smoothing · Residual Connection · Dense Connections
