14 research outputs found
Evaluation of header metadata extraction approaches and tools for scientific PDF documents
This paper evaluates the performance of tools for the extraction of metadata from scientific articles. Accurate metadata extraction is an important task for automating the management of digital libraries. This comparative study is a guide for developers looking to integrate the most suitable and effective metadata extraction tool into their software. We shed light on the strengths and weaknesses of seven tools in common use. In our evaluation using papers from the arXiv collection, GROBID delivered the best results, followed by Mendeley Desktop. SciPlore Xtract, PDFMeat, and SVMHeaderParse also delivered good results depending on the metadata type to be extracted
A Heuristic Baseline Method for Metadata Extraction from Scanned Electronic Theses and Dissertations
Extracting metadata from scholarly papers is an important text mining problem. Widely used open-source tools such as GROBID are designed for born-digital scholarly papers but often fail for scanned documents, such as Electronic Theses and Dissertations (ETDs). Here we present a preliminary baseline work with a heuristic model to extract metadata from the cover pages of scanned ETDs. The process started with converting scanned pages into images and then text files by applying OCR tools. Then a series of carefully designed regular expressions for each field is applied, capturing patterns for seven metadata fields: titles, authors, years, degrees, academic programs, institutions, and advisors. The method is evaluated on a ground truth dataset comprised of rectified metadata provided by the Virginia Tech and MIT libraries. Our heuristic method achieves an accuracy of up to 97% on the fields of the ETD text files. Our method poses a strong baseline for machine learning based methods. To our best knowledge, this is the first work attempting to extract metadata from non-born-digital ETDs
Obtaining Reference's Topic Congruity in Indonesian Publications using Machine Learning Approach
There are some criteria on how an article is categorized as a good article for publications. It could depend on some aspect like formatting and clarity, but mainly it depends on how the content of the article is constructed. The consistency of the topic that the article was written could show us how the authors construct the main idea in the article content. One indication that shows this consistency is congruity in the article’s topic and the topic of literature or reference cited in the document listed in the bibliography. This works attempting to automate the topic detection on the article’s references then obtain the congruity to the article title’s topic through metadata extraction and text classification. This is done by extracting metadata of an article file to obtain all possible reference title using GROBID than classify the topic using a supervised classification model. We found that some refinements in the whole approach should be considered in the next step of this work
Automatic Metadata Extraction Incorporating Visual Features from Scanned Electronic Theses and Dissertations
Electronic Theses and Dissertations (ETDs) contain domain knowledge that can
be used for many digital library tasks, such as analyzing citation networks and
predicting research trends. Automatic metadata extraction is important to build
scalable digital library search engines. Most existing methods are designed for
born-digital documents, so they often fail to extract metadata from scanned
documents such as for ETDs. Traditional sequence tagging methods mainly rely on
text-based features. In this paper, we propose a conditional random field (CRF)
model that combines text-based and visual features. To verify the robustness of
our model, we extended an existing corpus and created a new ground truth corpus
consisting of 500 ETD cover pages with human validated metadata. Our
experiments show that CRF with visual features outperformed both a heuristic
and a CRF model with only text-based features. The proposed model achieved
81.3%-96% F1 measure on seven metadata fields. The data and source code are
publicly available on Google Drive (https://tinyurl.com/y8kxzwrp) and a GitHub
repository (https://github.com/lamps-lab/ETDMiner/tree/master/etd_crf),
respectively.Comment: 7 pages, 4 figures, 1 table. Accepted by JCDL '21 as a short pape
On The Current State of Scholarly Retrieval Systems
The enormous growth in the size of scholarly literature makes its retrieval challenging. To address this challenge, researchers and practitioners developed several solutions. These include indexing solutions e.g. ResearchGate, Directory of Open Access Journals (DOAJ), Digital Bibliography & Library Project (DBLP) etc., research paper repositories e.g. arXiv.org, Zenodo, etc., digital libraries, scholarly retrieval systems, e.g., Google Scholar, Microsoft Academic Search, Semantic Scholar etc., digital libraries, and publisher websites. Among these, the scholarly retrieval systems, the main focus of this article, employ efficient information retrieval techniques and other search tactics. However, they are still limited in meeting the user information needs to the fullest. This brief review paper is an attempt to identify the main reasons behind this failure by reporting the current state of scholarly retrieval systems. The findings of this study suggest that the existing scholarly retrieval systems should differentiate scholarly users from ordinary users and identify their needs. Citation network analysis should be made an essential part of the retrieval system to improve the search precision and accuracy. The paper also identifies several research challenges and opportunities that may lead to better scholarly retrieval systems