Using argumentation to extract key sentences from biomedical abstracts

Automatically Identifying Key Sentences in Biomedical Abstracts Using Semi-Supervised Learning

Sentence retrieval for abstracts of randomized controlled trials

BACKGROUND The practice of evidence-based medicine (EBM) requires clinicians to integrate their expertise with the latest scientific research. But this is becoming increasingly difficult with the growing numbers of published articles. There is a clear need for better tools to improve clinician's ability to search the primary literature. Randomized clinical trials (RCTs) are the most reliable so...

Relevance of Cluster size in MMR based Summarizer : A Report

ion of documents by humans is complex to model as is any other information processing by humans. The abstracts differ from person to person, and usually vary in the style, language and detail. The process of abstraction is complex to be formulated mathematically or logically [14]. In the last decade some systems have been developed that generate abstractions using the latest natural language pr...

Functional gene clustering via gene annotation sentences, MeSH and GO keywords from biomedical literature

Gene function annotation remains a key challenge in modern biology. This is especially true for high-throughput techniques such as gene expression experiments. Vital information about genes is available electronically from biomedical literature in the form of full texts and abstracts. In addition, various publicly available databases (such as GenBank, Gene Ontology and Entrez) provide access to...

مقایسه روش‌های مختلف یادگیری ماشین در خلاصه‌سازی استخراجی گفتار به گفتار فارسی بدون استفاده از رونوشت

In this paper, extractive speech summarization using different machine learning algorithms was investigated. The task of Speech summarization deals with extracting important and salient segments from speech in order to access, search, extract and browse speech files easier and in a less costly manner. In this paper, a new method for speech summarization without using automatic speech recognitio...