Shapley Value Guided Extractive Text Summarization

Extractive summarization is a process that extracts salient sentences from the original document to create a summary. Typically, the process relies on sentence labels, which indicate whether a sentence should be included in the summary or not. Widely used sentence labels are constructed by greedily selecting sentences one by one from the original document. However, this greedy manner obtains sub-optimal sentence labels and can’t adequately distinguish the importance of individual sentences. Exploring new labeling schemes to construct superior and informative sentence labels is necessary. In this work, we take a cooperative game perspective on sentence label construction. We treat sentences as different players and label construction as a cooperative game between them. In such a perspective, we design an optimal and soft label, termed the Shapley Value of sentences (SVS) to reasonably discern the importance of individual sentences. To alleviate the computational complexity of SVS, we adopt a Monte Carlo sampling-based method to approximate it. Experimental results demonstrate that our proposed SVS label performs better on existing benchmarks across sentence-level and summary-level, in supervised and zero-shot settings.