Abstract
With the tremendous growth in the volume of information produced online every day (e.g. news articles), there is a need for automatic methods to identify related information about events as the events evolve over time (i.e., information threads). In this work, we propose a novel unsupervised approach, called HINT, which identifies coherent Hierarchical Information Threads. These threads can enable users to easily interpret a hierarchical association of diverse evolving information about an event or discussion. In particular, HINT deploys a scalable architecture based on network community detection to effectively identify hierarchical links between documents based on their chronological relatedness and answers to the 5W1H questions (i.e., who, what, where, when, why & how). On the NewSHead collection, we show that HINT markedly outperforms existing state-of-the-art approaches in terms of the quality of the identified threads. We also conducted a user study that shows that our proposed network-based hierarchical threads are significantly (\(p < 0.05\)) preferred by users compared to cluster-based sequential threads.
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Notes
- 1.
HINT’s code is available at: https://github.com/hitt08/HINT.
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Narvala, H., McDonald, G., Ounis, I. (2023). Effective Hierarchical Information Threading Using Network Community Detection. In: Kamps, J., et al. Advances in Information Retrieval. ECIR 2023. Lecture Notes in Computer Science, vol 13980. Springer, Cham. https://doi.org/10.1007/978-3-031-28244-7_44
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