ArticleSource code for biology and medicine2012
Layout-aware text extraction from full-text PDF of scientific articles.
Article in Source code for biology and medicine, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Efficient evidence selection for systematic reviews in traditional Chinese medicine.BMC medical research methodology · 2025Article
- A manually annotated corpus in French for the study of urbanization and the natural risk prevention.Scientific data · 2023Article
- PDFDataExtractor: A Tool for Reading Scientific Text and Interpreting Metadata from the Typeset Literature in the Portable Document Format.Journal of chemical information and modeling · 2022Article
- Automatic classification of literature in systematic reviews on food safety using machine learning.Current research in food science · 2022Article
- Review
- Text mining meets community curation: a newly designed curation platform to improve author experience and participation at WormBase.Database : the journal of biological databases and curation · 2020Article
- An effective biomedical document classification scheme in support of biocuration: addressing class imbalance.Database : the journal of biological databases and curation · 2019Article
- A comprehensive and quantitative comparison of text-mining in 15 million full-text articles versus their corresponding abstracts.PLoS computational biology · 2018Article
- MSL: Facilitating automatic and physical analysis of published scientific literature in PDF format.F1000Research · 2015Article
- Is Biblioleaks inevitable?Journal of medical Internet research · 2014Article
- Mining images in biomedical publications: Detection and analysis of gel diagrams.Journal of biomedical semantics · 2014Article
- NeuroElectro: a window to the world's neuron electrophysiology data.Frontiers in neuroinformatics · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
backgroundThe Portable Document Format (PDF) is the most commonly used file format for online scientific publications. The absence of effective means to extract text from these PDF files in a layout-aware manner presents a significant challenge for developers of biomedical text mining or biocuration informatics systems that use published literature as an information source. In this paper we introduce the 'Layout-Aware PDF Text Extraction' (LA-PDFText) system to facilitate accurate extraction of text from PDF files of research articles for use in text mining applications.
resultsOur paper describes the construction and performance of an open source system that extracts text blocks from PDF-formatted full-text research articles and classifies them into logical units based on rules that characterize specific sections. The LA-PDFText system focuses only on the textual content of the research articles and is meant as a baseline for further experiments into more advanced extraction methods that handle multi-modal content, such as images and graphs. The system works in a three-stage process: (1) Detecting contiguous text blocks using spatial layout processing to locate and identify blocks of contiguous text, (2) Classifying text blocks into rhetorical categories using a rule-based method and (3) Stitching classified text blocks together in the correct order resulting in the extraction of text from section-wise grouped blocks. We show that our system can identify text blocks and classify them into rhetorical categories with Precision1 = 0.96% Recall = 0.89% and F1 = 0.91%. We also present an evaluation of the accuracy of the block detection algorithm used in step 2. Additionally, we have compared the accuracy of the text extracted by LA-PDFText to the text from the Open Access subset of PubMed Central. We then compared this accuracy with that of the text extracted by the PDF2Text system, 2commonly used to extract text from PDF. Finally, we discuss preliminary error analysis for our system and identify further areas of improvement.
conclusionsLA-PDFText is an open-source tool for accurately extracting text from full-text scientific articles. The release of the system is available at http://code.google.com/p/lapdftext/.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.