Using LLM for Improving Key Event Discovery: Temporal-Guided News Stream Clustering with Event Summaries

Nishanth Sridhar Nakshatri, Siyi Liu, Sihao Chen, Dan Roth, Dan Goldwasser, Daniel Hopkins

Submission Type: Regular Short Paper

Submission Track: Computational Social Science and Cultural Analytics

Submission Track 2: NLP Applications

Keywords: news stream clustering, event discovery, political discourse characterization, LLM

TL;DR: We propose a versatile framework that leverages LLM to enhance current document clustering techniques for identifying key events within a vast collection of news articles.

Abstract: Understanding and characterizing the discus- sions around key events in news streams is important for analyzing political discourse. In this work, we study the problem of identification of such key events and the news articles associated with those events from news streams. We propose a generic framework for news stream clustering that analyzes the temporal trend of news articles to automatically extract the underlying key news events that draw significant media attention. We characterize such key events by generating event summaries, based on which we form document clusters in an unsupervised fashion. We evaluate our simple yet effective framework, and show that it produces more coherent event-focused clusters. To demonstrate the utility of our approach, and facilitate future research along the line, we use our framework to construct KeyEvents, a dataset of 40k articles with 611 key events from 11 topics.

Submission Number: 5373