This paper introduces STRASS: Summarization by TRAnsformation Selection and Scoring. It is an extractive text summarization method which leverages the semantic information in existing sentence embedding spaces. Our method creates an extractive summary by selecting the sentences with the closest embeddings to the document embedding. The model learns a transformation of the document embedding to minimize the similarity between the extractive summary and the ground truth summary. As the transformation is only composed of a dense layer, the training can be done on CPU, therefore, inexpensive. Moreover, inference time is short and linear according to the number of sentences. As a second contribution, we introduce the French CASS dataset, composed of judgments from the French Court of cassation and their corresponding summaries. On this dataset, our results show that our method performs similarly to the state of the art extractive methods with effective training and inferring time.

本研究介绍了 STRASS 方法来进行提取式文本摘要，其利用现有句子嵌入空间中的语义信息来选择最接近文档嵌入的句子并进行提取式摘要。同时，该方法在法国判例摘要数据集上进行了有效的训练和推断，表现良好。

STRASS: 基于句子嵌入的轻量高效提取式摘要方法