The automatic text summarization (ATS) task consists in automatically synthesizing a document to provide a condensed version of it. Creating a summary requires not only selecting the main topics of the sentences but also identifying the key relationships between these topics. Related works rank text units (mainly sentences) to select those that could form the summary. However, the resulting summaries may not include all the topics covered in the source text because important information may have been discarded. In addition, the semantic structure of documents has been barely explored in this field. Thus, this study proposes a new method for the ATS task that takes advantage of semantic information to improve keyword detection. This proposed method increases not only the coverage by clustering the sentences to identify the main topics in the source document but also the precision by detecting the keywords in the clusters. The experimental results of this work indicate that the proposed method outperformed previous methods with a standard collection.

The fitness function is a type of objective function that quantifies the optimality of a solution; the correct formulation of this function is relevant, in evolutionary-based ATS systems, because it must indicate the quality of the summaries. Several unsupervised evolutionary methods for the automatic text summarization (ATS) task proposed in current standards require authors to manually construct an objective function that guides the algorithms to create good-quality summaries. In this sense, it is necessary to test each fitness function created to measure its performance; however, this process is time consuming and only a few functions are analyzed. This study proposes the automatic generation of heuristic functions, through genetic programming (GP), to be applied in the ATS task. Therefore, our proposed method for ATS provides an automatically generated fitness function for cluster-based unsupervised approaches. The results of this study, using two standard collections, demonstrate to automatically obtain an orientation function that leads to good quality abstracts.

We identify deceptive text by using different kinds of features: A continuous semantic space model based on latent Dirichlet allocation topics (LDA), one-hot representation (OHR), syntactic information from syntactic n-grams (SN), and lexicon-based f

In this work we propose to solve the author verification problem using a semantic space model through Latent Dirichlet Allocation (LDA). We experiment with the corpus used in the author identification tasks at PAN 2014 and PAN 2015. These datasets consist of subsets in the following languages: English, Spanish, Dutch and Greek. Each problem contained in these corpora is formed by one to five known documents which were written by one author and one unknown document. The task is to predict whether the unknown document was written by the author who wrote the known documents. We processed the documents in the dataset and captured the fingerprint of authors by generating a probabilistic distribution of words in the documents. In PAN 2015 classification, we achieved 81.6%, 75.4%, 74.1%, 67.1% accuracy for each English, Spanish, Dutch and Greek subset respectively. In particular for the English subset, we outreached the best result reported in both competitions.