# Improving Semantic Relevance for Sequence-to-Sequence Learning of Chinese Social Media Text Summarization

arXiv: https://arxiv.org/abs/1706.02459 · HTML: https://mlsanity.com/paper/1706.02459/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2017.

## Built on by (most cited first, 2 of 2)

- [A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methods](https://mlsanity.com/paper/2403.02901/) (2024)
- [Brand Celebrity Matching Model Based on Natural Language Processing](https://mlsanity.com/paper/2208.08887/) (2022)

## Abstract (arXiv)

Current Chinese social media text summarization models are based on an encoder-decoder framework. Although its generated summaries are similar to source texts literally, they have low semantic relevance. In this work, our goal is to improve semantic relevance between source texts and summaries for Chinese social media summarization. We introduce a Semantic Relevance Based neural model to encourage high semantic similarity between texts and summaries. In our model, the source text is represented by a gated attention encoder, while the summary representation is produced by a decoder. Besides, the similarity score between the representations is maximized during training. Our experiments show that the proposed model outperforms baseline systems on a social media corpus.