Optimization in extractive summarization processes through automatic classification

Abstract

The results of an extractive automatic summarization task depends to a great extend on the nature of the processed texts (e.g., news, medicine, or literature). In fact, general-purpose methods usually need to be adhoc modified to improve their performance when dealing with a particular application context. However, this customization requires a lot of effort from domain experts and application developers, which makes it not always possible nor appropriate. In this paper, we propose a multi-language approach to extractive summarization which adapts itself to different text domains in order to improve its performance. In a training step, our approach leverages the features of the text documents in order to classify them by using machine learning techniques. Then, once the text typology of each text is identified, it tunes the different parameters of the extraction mechanism solving an optimization problem for each of the text document classes. This classifier along with the learned optimizations associated with each document class allows our system to adapt to each of the input texts automatically. The proposed method has been applied in a real environment of a media company with promising results.