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Sports Game Summarization Based on Sub-events and Game-Changing Phrases

Part of the Studies in Computational Intelligence book series (SCI,volume 742)


Microblogs are one of the most important resources for natural language processing. This paper describes a summarization task of sports events on Twitter. We focus on an abstractive approach based on sub-events in the sports event. Abstractive summaries usually are better than summaries generated by extractive approaches in terms of readability. Furthermore, our method can incorporate sophisticated phrases that explain the scene. First, our method detects burst situations in which many users post tweets when a sub-event in a game occurs. Tweets in the burst situations are the inputs of our method. Next, it extracts sub-event elements (SEEs) that contain actions in a game, such as “Player A made a pass to Player B” and “Player B made a shot on goal.” Then, it identifies the optimal order of the extracted SEEs by using a scoring method. Finally, it generates an abstractive summary on the basis of the ordered SEEs, such as “Playler B made a shot on goal from the Player A’s pass.” In addition, it adds game-changing phrases into the abstractive summary by some rules. In the experiment, we show the effectiveness of our method as compared with related work based on an extractive approach.

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  • DOI: 10.1007/978-3-319-70636-8_5
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  1. 1.

    Note that our target is Japanese tweets although the examples are written in English.

  2. 2.

    The target of this process is only some combinations of words. For example, “a team name \(+\) a specific word”; Japan goal should be Japan’s goal .

  3. 3.

    In addition, we also use some rules. For example, the SEE “goal” follows the SEE “assist”.

  4. 4.

    The numbers are the frequencies of each SEE.

  5. 5.

    Note that these items with “<” and “>” are not SEEs. They are just dependency relations between words in tweets.

  6. 6.

    In the baseball task, Inning Phrases (IP).

  7. 7. .

  8. 8.

  9. 9.

  10. 10.

    1: low readability to 4: high readability.

  11. 11.

    In the experiment, no definition about the readability was given to the test subjects. The questionnaire just contained the sentence “Evaluate the readability of each document.”

  12. 12.

    This integration is not realized in the current method because of the conflict with basic policies in Sect. 3.2.2.


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Tagawa, Y., Shimada, K. (2018). Sports Game Summarization Based on Sub-events and Game-Changing Phrases. In: Matsuo, T., Mine, T., Hirokawa, S. (eds) New Trends in E-service and Smart Computing. Studies in Computational Intelligence, vol 742. Springer, Cham.

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