OTExtSum: Extractive Text Summarisation with Optimal Transport
This addresses the need for interpretable and training-free summarization methods for NLP applications, though it is incremental as it adapts an existing mathematical framework to a new task.
The authors tackled extractive text summarization by formulating it as an Optimal Transport problem, proposing OTExtSum, which outperformed state-of-the-art non-learning-based and some learning-based methods on ROUGE scores across four datasets.
Extractive text summarisation aims to select salient sentences from a document to form a short yet informative summary. While learning-based methods have achieved promising results, they have several limitations, such as dependence on expensive training and lack of interpretability. Therefore, in this paper, we propose a novel non-learning-based method by for the first time formulating text summarisation as an Optimal Transport (OT) problem, namely Optimal Transport Extractive Summariser (OTExtSum). Optimal sentence extraction is conceptualised as obtaining an optimal summary that minimises the transportation cost to a given document regarding their semantic distributions. Such a cost is defined by the Wasserstein distance and used to measure the summary's semantic coverage of the original document. Comprehensive experiments on four challenging and widely used datasets - MultiNews, PubMed, BillSum, and CNN/DM demonstrate that our proposed method outperforms the state-of-the-art non-learning-based methods and several recent learning-based methods in terms of the ROUGE metric.