SportsSum2.0: Generating High-Quality Sports News from Live Text Commentary

• URL: http://arxiv.org/abs/2110.05750v1
• Date: Tue, 12 Oct 2021 05:39:48 GMT
• Title: SportsSum2.0: Generating High-Quality Sports News from Live Text Commentary
• Authors: Jiaan Wang, Zhixu Li, Qiang Yang, Jianfeng Qu, Zhigang Chen, Qingsheng Liu, Guoping Hu
• Abstract summary: Sports game summarization aims to generate news articles from live text commentaries. Recent work, SportsSum, not only constructs a large benchmark dataset, but also proposes a two-step framework. In this paper, we publish a new benchmark dataset SportsSum2.0, together with a modified summarization framework.
• Score: 18.52461327269355
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sports game summarization aims to generate news articles from live text commentaries. A recent state-of-the-art work, SportsSum, not only constructs a large benchmark dataset, but also proposes a two-step framework. Despite its great contributions, the work has three main drawbacks: 1) the noise existed in SportsSum dataset degrades the summarization performance; 2) the neglect of lexical overlap between news and commentaries results in low-quality pseudo-labeling algorithm; 3) the usage of directly concatenating rewritten sentences to form news limits its practicability. In this paper, we publish a new benchmark dataset SportsSum2.0, together with a modified summarization framework. In particular, to obtain a clean dataset, we employ crowd workers to manually clean the original dataset. Moreover, the degree of lexical overlap is incorporated into the generation of pseudo labels. Further, we introduce a reranker-enhanced summarizer to take into account the fluency and expressiveness of the summarized news. Extensive experiments show that our model outperforms the state-of-the-art baseline.

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