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Sports game summarization aims to generate news articles from live textcommentaries. A recent state-of-the-art work, SportsSum, not only constructs alarge benchmark dataset, but also proposes a two-step framework. Despite itsgreat contributions, the work has three main drawbacks: 1) the noise existed inSportsSum dataset degrades the summarization performance; 2) the neglect oflexical overlap between news and commentaries results in low-qualitypseudo-labeling algorithm; 3) the usage of directly concatenating rewrittensentences to form news limits its practicability. In this paper, we publish anew benchmark dataset SportsSum2.0, together with a modified summarizationframework. In particular, to obtain a clean dataset, we employ crowd workers tomanually clean the original dataset. Moreover, the degree of lexical overlap isincorporated into the generation of pseudo labels. Further, we introduce areranker-enhanced summarizer to take into account the fluency andexpressiveness of the summarized news. Extensive experiments show that ourmodel outperforms the state-of-the-art baseline.

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