Long-Span Language Models for Query-Focused Unsupervised Extractive Text Summarization

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 Across Sentence Boundary LSTM-based LMs, ASB LSTM and biASB 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.