Exploring the Incorporation of Opinion Polarity for Abstractive Multi-document Summarisation

Ramsauer, Dominik; Kruschwitz, Udo

doi:10.1007/978-3-030-72240-1_35

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…This dataset was augmented with polarity labels according to the news source label. The MPQA Opinion Corpus was chosen over other sentiment analysis datasets as it consists of articles pulled from news articles on a broad range of news sources and is consistent with the approach used in (Ramsauer and Kruschwitz, 2021).…”

Section: Datasetsmentioning

confidence: 99%

ACM -- Attribute Conditioning for Abstractive Multi Document Summarization

Sankar¹,

Chadha²

2022

Preprint

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ive multi document summarization has evolved as a task through the basic sequence to sequence approaches to transformer and graph based techniques. Each of these approaches has primarily focused on the issues of multi document information synthesis and attention based approaches to extract salient information. A challenge that arises with multi document summarization which is not prevalent in single document summarization is the need to effectively summarize multiple documents that might have conflicting polarity, sentiment or subjective information about a given topic. In this paper we propose ACM, attribute conditioned multi document summarization,a model that incorporates attribute conditioning modules in order to decouple conflicting information by conditioning for a certain attribute in the output summary. This approach shows strong gains in ROUGE score over baseline multi document summarization approaches and shows gains in fluency, informativeness and reduction in repetitiveness as shown through a human annotation analysis study.

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Section: Datasetsmentioning

confidence: 99%