Text Summarization and Translation of Summarized Outcome in French

Abstract

Automatic text summarization is increasingly required with the exponential growth of unstructured text through increasing internet and social media usage across the globe. The various approaches are outcomes of extraction-based and abstraction-based. In Extraction-based summarization, the extracted content from the original data, is typically presented in the same or slightly modified form without significant paraphrasing or restructuring. Abstractive methods involve building an internal representation of the original content and then using that representation to generate a summary that may not be present in the original text verbatim. They employ natural language processing techniques, such as language generation models, to paraphrase and rephrase sections of the source document to create a more human-like summary. Abstraction in abstractive summarization is indeed a challenging task because it requires not only linguistic and syntactic understanding but also a deeper semantic understanding of the original content. The evaluation of automatically summarized text through criteria of precision and recall, and various evaluation methods, datasets used for domain based and generic text summarization. This paper proposes the Sentence Length Impact (SLI) algorithm to summarize English text content, which gives 92% accuracy and translating the same in French.

Keywords

• automatic text summarization
• automatic text translation
• neural network
• machine learning
• rogue evaluation
• pos tagging
• natural language processing
• sentence length impact