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Gabriel de França Pereira e Silva

Researcher at Federal University of Pernambuco

Publications -  31
Citations -  557

Gabriel de França Pereira e Silva is an academic researcher from Federal University of Pernambuco. The author has contributed to research in topics: Automatic summarization & Digital image processing. The author has an hindex of 8, co-authored 31 publications receiving 457 citations. Previous affiliations of Gabriel de França Pereira e Silva include Universidade Federal Rural de Pernambuco.

Papers
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Journal ArticleDOI

Assessing sentence scoring techniques for extractive text summarization

Rafael Ferreira, +8 more
- 15 Oct 2013 - 
TL;DR: A quantitative and qualitative assessment of 15 algorithms for sentence scoring available in the literature are described and directions to improve the sentence extraction results obtained are suggested.
Journal ArticleDOI

A multi-document summarization system based on statistics and linguistic treatment

Rafael Ferreira, +7 more
- 01 Oct 2014 - 
TL;DR: This paper presents a multi-document summarization system that concisely extracts the main aspects of a set of documents, trying to avoid the typical problems of this type of summarization: information redundancy and diversity.
Proceedings Article

A Four Dimension Graph Model for Automatic Text Summarization.

Rafael Ferreira, +7 more
TL;DR: In this paper, a new graph model for text summarization is proposed, which relies on four dimensions (similarity, semantic similarity, co reference, discourse information) to create the graph.
Book ChapterDOI

Shading Removal of Illustrated Documents

Daniel M. Oliveira, +3 more
TL;DR: An algorithm to remove the shading of documents that have large non-uniform regions such as photographs and illustrations is proposed once the background is identified, Natural Neighbor Interpolation estimates the shading for non-background pixels.
Proceedings ArticleDOI

Automatic Text Document Summarization Based on Machine Learning

Gabriel de França Pereira e Silva, +6 more
TL;DR: In this article, the authors used machine learning techniques to assess the quality of the twenty most referenced strategies used in extractive summarization, integrating them in a tool for automatic generation of summaries.