News Topic Sentence Generation Based on Two-stage Summarization

Jingwen Lin, Shunan Zang, Taoyu Su, Chuang Zhang, Tingwen Liu

Abstract: With the rapid development of the Internet, social media platforms are filled with a large amount of article information, so the task of topic generation, which can greatly improve browsing and reading efficiency, has gradually developed. Currently, manually organizing and generating topic sentences requires a lot of human resources, and the previous researches on automatic topic generation suffer from poor effectiveness, poor readability and high redundancy due to the generation of topic words only. To address these problems, our work introduces the maturing text summarization technology into the automatic topic sentence generation task to improve the coherence and readability of the generated topic sentences. We present a two-stage summary-based news topic sentence generation model, TTSG, which acquires summaries from each article, clusters and sorts the summaries, and then performs a secondary summary to obtain the final topic sentence. Experimental results show that TTSG outperforms the baseline model and generates more coherent and readable topic sentences. Our code is available at https://github.com/sutaoyu/TTSG.