Compressão de sentenças sobre domínios com disponibilidade limitada de dados rotulados

Abstract

Huge volumes of data are produced every day on the Web. These are a big amount of videos, images, and texts that store unstructured information. Text summarization systems were created to facilitate the presentations of large amounts of textual data as well as to aid information retrieval over this type of data. The sentence compression has been developed due to the need for better summaries generated by these systems. However, when trained over domains with restricted amounts of labeled data for sentence compression, neural netword-based models tend to not be able to extract important features. Thus, to improve the performance of these models in this scenario, some pieces of information must be extracted and adapted before being used for training. Thus, we propose a sentence compression model capable of achieving competitive results, even when trained with smaller amounts of data, compared with other neural networkbased models, by using a set of linguistic features extracted from words alongside a rare words reduction strategy over the sentences.