Abstract
Abstractive multi-document summarisation (MDS) remains a challenging task. Part of the problem is the question as to how to preserve a document’s polarity in the summary. We propose an opinion polarity attention model for MDS, which incorporates a polarity estimator based on a BERT-GRU sentiment analysis network. It captures the impact of opinions expressed in the source documents and integrates it in the attention mechanism. Experimental results using a state-of-the-art MDS approach and a common benchmark test collection demonstrate that this model has a measurable positive effect using a range of metrics.
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Notes
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All code, model files and outputs are available at https://github.com/dramsauer/Summarizing-Opinions.
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A conceivable way to penalise opinionated content might be to choose \(1-|tanh(v{_i})|\).
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The basic PG-BRRN approach was also reproduced with similarly lower results, which raises the question of reproducibility in general. There have also been attempts to reproduce MGSum and GraphSum, but without success.
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Ramsauer, D., Kruschwitz, U. (2021). Exploring the Incorporation of Opinion Polarity for Abstractive Multi-document Summarisation. In: Hiemstra, D., Moens, MF., Mothe, J., Perego, R., Potthast, M., Sebastiani, F. (eds) Advances in Information Retrieval. ECIR 2021. Lecture Notes in Computer Science(), vol 12657. Springer, Cham. https://doi.org/10.1007/978-3-030-72240-1_35
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