Event-Driven News Stream Clustering using Entity-Aware Contextual Embeddings

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics, pages 2330–2340 April 19 - 23, 2021. ©2021 Association for Computational Linguistics 2330 Event-Driven News Stream Clustering using Entity-Aware Contextual Embeddings Kailash Karthik Saravanakumar∗2 Miguel Ballesteros1 Muthu Kumar Chandrasekaran1 Kathleen McKeown1,2 1Amazon AI, USA 2Department of Computer Science, Columbia University, NY, USA {ballemig, cmuthuk, mckeownk}@amazon.com {kailashkarthik.s}@columbia.edu Abstract We propose a method for online news stream clustering that is a variant of the non- parametric streaming K-means algorithm. Our model uses a combination of sparse and dense document representations, aggregates document-cluster similarity along these mul- tiple representations and makes the cluster- ing decision using a neural classifier. The weighted document-cluster similarity model is learned using a novel adaptation of the triplet loss into a linear classification objective. We show that the use of a suitable fine-tuning ob- jective and external knowledge in pre-trained transformer models yields significant improve- ments in the effectiveness of contextual em- beddings for clustering. Our model achieves a new state-of-the-art on a standard stream clus- tering dataset of English documents. 1 Introduction Human presentation and understanding of news ar- ticles is almost never isolated. Seminal real-world events spawn a chain of strongly correlated news articles that form a news story over time. Given the abundance of online news sources, the con- sumption of news in the context of the stories they belong to is challenging. Unless people are able to scour the many news sources multiple times a day, major events of interest can be missed as they occur. The real-time monitoring of news, segregat- ing articles into their corresponding stories, thus enables people to follow news stories over time. This goal of identifying and tracking topics from a news stream was first introduced in the Topic Detection and Tracking (TDT) task (Allan et al., 1998). Topics in the news stream setting usually correspond to real-world events, while news ar- ticles may also be categorized thematically into ∗Work done during internship at Amazon sports, politics, etc. We focus on the task of cluster- ing news on the basis of event-based story chains. We make a distinction between our definition of an event topic, which follows TDT and refers to large-scale real-world events, and the fine-grained events used in trigger-based event detection (Ahn, 2006). Given the non-parametric nature of our task (the number of events is not known before- hand and evolves over time), the two primary ap- proaches have been topic modeling using Hierar- chical Dirichlet Processes (HDPs) (Teh et al., 2005; Beykikhoshk et al., 2018) and Stream Clustering (MacQueen, 1967; Laban and Hearst, 2017; Mi- randa et al., 2018). While HDPs use word distri- butions within documents to infer topics, stream clustering models use representation strategies to encode and cluster documents. Contemporary mod- els have adopted stream clustering using TF-IDF weighted bag of words representations to achieve state-of-the-art results (Staykovski et al., 2019). In this paper, we present a model for event topic detection and tracking from news streams that lever- …

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