An adaptation of a F-measure for automatic text summarization by extraction
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An adaptation of a F-measure for automatic text summarization by extraction Mohamed Amine Boudia1
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Reda Mohamed Hamou1 • Abdelmalek Amine1 • Ahmed Chaouki Lokbani1
Received: 5 April 2019 / Revised: 21 July 2019 / Accepted: 14 November 2019 Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract In this paper, we propose to adapt the F-measure to evaluate an automatic summaries of texts; we the main key to our proposal is to prove that the automatic summary task can be modeled in supervised classification. First, we will start the research by to make a comparison between the automatic summary task and the supervised classification. After that, we are going to define how to draw a confusion matrix which classification evaluation base, and from it we calculate the F-Measure. In this vein, we must prove that the new measure is valid and trustworthy, And for this we will calculates the correlation with ROUGE Evaluation. Before ending we analyze and interpret the main results in order to answer the questions put forward in this research. At the end, we are going to conclude our study with a set of facts based on the collected data. Keywords Automatic summary extraction Text mining Evaluation Automatic language processing F-measure Correlation ROUGE
1 Introduction and problematic
Ahmed Chaouki Lokbani [email protected]
text summarization. It is one of tasks that helps us to navigate the entire document and allow the extraction of the important information effectively and the representative sentences, and also capture the salient details. Text summarization is an important activity in the analysis of a high volume text documents and is currently a major research topic in NLP [1]. Automatic Text Summarization refer to the process of distilling the most important information from a source (or sources) to produce an abridged version for a particular user (or users) and task (or tasks) [2]. In another word it is the process of creating a short version of a longer document but not only with some words and phrases that the original document contains but the goal is to retrieve the essence of the source document to form a coherent version that can be accurate and should be easy to read fluently as a new independent document [3]. ‘‘Automatic text summarization is the task of producing a concise and fluent summary while preserving key information content and overall meaning’’ [4]. In [5] the authors provide a list of automatic summarization examples such as:
GeCoDe Lab, Department of Computer, University of SAI¨DA - Dr. Moulay Tahar, Saida, Algeria
• Previews (of movies) • Reviews (of a book, CD, movie, etc.)
With the growth of the amount of the data nowadays, the textual form takes the largest part of it and that makes the need for different tools and mechanisms to extract meaningful information has become very critical. Web pages, news articles, blogs and much more types of long documents that exists on the internet often cause trivial w
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