Abstract
Effective unsupervised query-focused extractive summarization systems use query-specific features along with short-range language models (LMs) in sentence ranking and selection summarization subtasks. We hypothesize that applying long-span n-gram-based and neural LMs that better capture larger context can help improve these subtasks. Hence, we outline the first attempt to apply long-span models to a query-focused summarization task in an unsupervised setting. We also propose the A cross S entence B oundary LSTM-based LMs, ASB LSTM and bi ASB LSTM, that is geared towards the query-focused summarization subtasks. Intrinsic and extrinsic experiments on a real word corpus with 100 Wikipedia event descriptions as queries show that using the long-span models applied in an integer linear programming (ILP) formulation of MMR criterion are the most effective against several state-of-the-art baseline methods from the literature.
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Singh, M., Mishra, A., Oualil, Y., Berberich, K., Klakow, D. (2018). Long-Span Language Models for Query-Focused Unsupervised Extractive Text Summarization. In: Pasi, G., Piwowarski, B., Azzopardi, L., Hanbury, A. (eds) Advances in Information Retrieval. ECIR 2018. Lecture Notes in Computer Science(), vol 10772. Springer, Cham. https://doi.org/10.1007/978-3-319-76941-7_59
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