  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.
