Discriminative Marginalized Probabilistic Neural Method for Multi-Document Summarization of Medical Literature

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Abstract

Although current state-of-the-art Transformer-based solutions succeeded in a wide range for single-document NLP tasks, they still struggle to address multi-input tasks such as multi-document summarization. Many solutions truncate the inputs, thus ignoring potential summary-relevant contents, which is unacceptable in the medical domain where each information can be vital. Others leverage linear model approximations to apply multi-input concatenation, worsening the results because all information is considered, even if it is conflicting or noisy with respect to a shared background. Despite the importance and social impact of medicine, there are no ad-hoc solutions for multi-document summarization. For this reason, we propose a novel discriminative marginalized probabilistic method (DAMEN) trained to discriminate critical information from a cluster of topic-related medical documents and generate a multi-document summary via token probability marginalization. Results prove we outperform the previous state-of-the-art on a biomedical dataset for multi-document summarization of systematic literature reviews. Moreover, we perform extensive ablation studies to motivate the design choices and prove the importance of each module of our method.

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APA

Moro, G., Ragazzi, L., Valgimigli, L., & Freddi, D. (2022). Discriminative Marginalized Probabilistic Neural Method for Multi-Document Summarization of Medical Literature. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (Vol. 1, pp. 180–189). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2022.acl-long.15

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