# Knowledge Enhanced Sports Game Summarization

arXiv: https://arxiv.org/abs/2111.12535 · HTML: https://mlsanity.com/paper/2111.12535/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 28 references that are in the map.
- First posted on arXiv 24 Nov 2021.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016)
- [A Neural Attention Model for Abstractive Sentence Summarization](https://mlsanity.com/paper/1509.00685/) (2015)
- [FLAT: Chinese NER Using Flat-Lattice Transformer](https://mlsanity.com/paper/2004.11795/) (2020)
- [Sentence Centrality Revisited for Unsupervised Summarization](https://mlsanity.com/paper/1906.03508/) (2019)
- [SportsSum2.0: Generating High-Quality Sports News from Live Text Commentary](https://mlsanity.com/paper/2110.05750/) (2021)

## Built on by (most cited first, 9 of 9)

- [A Survey on Cross-Lingual Summarization](https://mlsanity.com/paper/2203.12515/) (2022)
- [SPORTU: A Comprehensive Sports Understanding Benchmark for Multimodal Large Language Models](https://mlsanity.com/paper/2410.08474/) (2024)
- [Long-Document Cross-Lingual Summarization](https://mlsanity.com/paper/2212.00586/) (2022)
- [AspectMMKG: A Multi-modal Knowledge Graph with Aspect-aware Entities](https://mlsanity.com/paper/2308.04992/) (2023)
- [RT-KGD: Relation Transition Aware Knowledge-Grounded Dialogue Generation](https://mlsanity.com/paper/2207.08212/) (2022)
- [GOAL: Towards Benchmarking Few-Shot Sports Game Summarization](https://mlsanity.com/paper/2207.08635/) (2022)
- [Snowman: A Million-scale Chinese Commonsense Knowledge Graph Distilled from Foundation Model](https://mlsanity.com/paper/2306.10241/) (2023)
- [Towards Automatic Soccer Commentary Generation with Knowledge-Enhanced Visual Reasoning](https://mlsanity.com/paper/2604.00057/) (2026)
- [Towards Consistency Filtering-Free Unsupervised Learning for Dense Retrieval](https://mlsanity.com/paper/2308.02926/) (2023)

## Abstract (arXiv)

Sports game summarization aims at generating sports news from live commentaries. However, existing datasets are all constructed through automated collection and cleaning processes, resulting in a lot of noise. Besides, current works neglect the knowledge gap between live commentaries and sports news, which limits the performance of sports game summarization. In this paper, we introduce K-SportsSum, a new dataset with two characteristics: (1) K-SportsSum collects a large amount of data from massive games. It has 7,854 commentary-news pairs. To improve the quality, K-SportsSum employs a manual cleaning process; (2) Different from existing datasets, to narrow the knowledge gap, K-SportsSum further provides a large-scale knowledge corpus that contains the information of 523 sports teams and 14,724 sports players. Additionally, we also introduce a knowledge-enhanced summarizer that utilizes both live commentaries and the knowledge to generate sports news. Extensive experiments on K-SportsSum and SportsSum datasets show that our model achieves new state-of-the-art performances. Qualitative analysis and human study further verify that our model generates more informative sports news.