Extracting Information-rich Part of Texts using Text Denoising

TL;DR

This paper introduces Text Denoising, a novel technique that reduces large text volumes to highlight information-rich content, improving tasks like relation extraction and keyphrase indexing in biomedical texts.

Contribution

The paper proposes a new text reduction method called Text Denoising that emphasizes information-rich content using a readability index, especially effective in biomedical text processing.

Findings

Text Denoising improves information extraction accuracy.

Reduced texts are more relevant for biomedical relation tasks.

The technique enhances keyphrase indexing efficiency.

Abstract

The aim of this paper is to report on a novel text reduction technique, called Text Denoising, that highlights information-rich content when processing a large volume of text data, especially from the biomedical domain. The core feature of the technique, the text readability index, embodies the hypothesis that complex text is more information-rich than the rest. When applied on tasks like biomedical relation bearing text extraction, keyphrase indexing and extracting sentences describing protein interactions, it is evident that the reduced set of text produced by text denoising is more information-rich than the rest.