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- W4300817832 abstract "Opinion summarization is the task of automatically generating summaries for a set of reviews about a specific target (e.g., a movie or a product). Since the number of reviews for each target can be prohibitively large, neural network-based methods follow a two-stage approach where an extractive step first pre-selects a subset of salient opinions and an abstractive step creates the summary while conditioning on the extracted subset. However, the extractive model leads to loss of information which may be useful depending on user needs. In this paper we propose a summarization framework that eliminates the need to rely only on pre-selected content and waste possibly useful information, especially when customizing summaries. The framework enables the use of all input reviews by first condensing them into multiple dense vectors which serve as input to an abstractive model. We showcase an effective instantiation of our framework which produces more informative summaries and also allows to take user preferences into account using our zero-shot customization technique. Experimental results demonstrate that our model improves the state of the art on the Rotten Tomatoes dataset and generates customized summaries effectively." @default.
- W4300817832 created "2022-10-04" @default.
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- W4300817832 date "2019-09-05" @default.
- W4300817832 modified "2023-09-27" @default.
- W4300817832 title "Informative and Controllable Opinion Summarization" @default.
- W4300817832 doi "https://doi.org/10.48550/arxiv.1909.02322" @default.
- W4300817832 hasPublicationYear "2019" @default.
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