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SNIL: Generating Sports News From Insights With Large Language Models.

Liqi Cheng, Dazhen Deng, Xiao Xie

IEEE Transactions on Visualization and Computer Graphics

|April 23, 2024

Summary

This summary is machine-generated.

This study introduces SNIL, an AI system for automatically generating sports news from data insights. It helps journalists by creating narrative structures and visualizations, bridging analysis and communication tasks.

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Area of Science:

Computational Linguistics
Human-Computer Interaction
Data Journalism

Background:

Data journalism increasingly relies on advanced data analysis and visualizations, demanding higher journalist skillsets.
Existing visual analytics tools primarily focus on insight generation, with limited support for journalistic dissemination.
Bridging the gap between data analysis and communicative tasks is crucial for effective data news creation.

Purpose of the Study:

To facilitate the automatic creation of sports news from natural language insights.
To develop a system that supports journalists in transforming data insights into engaging news content.
To explore a workflow for generating narrative structures and communicative visualizations for sports journalism.

Main Methods:

Conducted a preliminary study on published sports articles to identify key journalistic elements.
Proposed a four-step workflow: data space exploration, narrative structure generation, episode creation, and visualization mapping.
Implemented SNIL, a human-AI interaction system using large language models (LLMs) and user input.
Enabled modification of textual and graphical content within an episode-based structure.

Main Results:

Demonstrated the usability of the SNIL system through user studies.
Showcased the system's effectiveness in bridging the gap between data analysis and communicative tasks.
Gathered positive feedback from both sports experts and fans regarding the system's benefits.

Conclusions:

SNIL effectively supports the automatic generation of sports news from data insights.
The human-AI interaction approach enhances the creation of informative and appealing data news.
The system provides a valuable tool for journalists, streamlining the process from data analysis to news dissemination.