Aspect-Controllable Opinion Summarization

By Reinald Kim Amplayo, Stefanos Angelidis, Mirella Lapata. 2021

84 citations recorded by the source corpus.

Recent work on opinion summarization produces general summaries based on a set of input reviews and the popularity of opinions expressed in them. In this paper, we propose an approach that allows the generation of customized summaries based on aspect queries (e.g., describing the location and room of a hotel). Using a review corpus, we create a synthetic training dataset of (review, summary) pairs enriched with aspect controllers which are induced by a multi-instance learning model that predicts the aspects of a document at different levels of granularity. We fine-tune a pretrained model using our synthetic dataset and generate aspect-specific summaries by modifying the aspect controllers. Experiments on two benchmarks show that our model outperforms the previous state of the art and generates personalized summaries by controlling the number of aspects discussed in them.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Text (2022, methodology): “…that best-worst scaling (asking evaluators to choose the best and the worst items in a set) is an efficient and reliable method for collecting annotations, and this approach has been used to collect comparative evaluations of generated text (e.g., Liu and Lapata, 2019; Amplayo et al., 2021).”