PDFdigest: an Adaptable Layout-Aware PDF-to-XML Textual Content Extractor for Scientific Articles (L18-1)
Copied to clipboard
| Challenge: | Existing tools to extract structured textual content from PDFs are essential to enable scientific text mining. |
| Approach: | They propose a PDF-to-XML textual content extraction tool that extracts structured textual contents from scientific articles in PDF format. |
| Outcome: | The proposed tool extracts structured textual content from scientific articles in PDF format while preserving both the textual contents and layout details of the input PDF document. |
Similar Papers
VILA: Improving Structured Content Extraction from Scientific PDFs Using Visual Layout Groups (2022.tacl-1)
Copied to clipboard
| Challenge: | Recent work has improved extraction accuracy by incorporating elementary layout information, for example, each token’s 2D position on the page, into language model pretraining. |
| Approach: | They propose a method that explicitly models VIsual LAyout (VILA) groups, that is, text lines or text blocks, to further improve extraction accuracy. |
| Outcome: | The proposed methods show that inserting special tokens denoting layout group boundaries can lead to a 1.9% Macro F1 improvement in token classification. |
PDF-to-Text Reanalysis for Linguistic Data Mining (L18-1)
Copied to clipboard
| Challenge: | In the 1990s, extracting semistructured text from scientific writing in PDF files was largely a computer vision and OCR problem. |
| Approach: | They propose a system for the reanalysis of PDF-extracted text that performs block detection, respacing, and tabular data analysis for linguistic data mining. |
| Outcome: | The proposed system eliminates the extreme verbosity of XML output while leaving important positional information available for downstream processes. |
PDF-to-Tree: Parsing PDF Text Blocks into a Tree (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies try to extract one universal reading order for PDF files, however, some applications, like Retrieval Augmented Generation, require breaking long articles into sections and subsections for better indexing. |
| Approach: | They propose a new task and dataset, PDF-to-Tree, which organizes the text blocks of a PDF into a tree structure. |
| Outcome: | The proposed parser achieves 93.93% accuracy, surpassing baseline methods by 6.72%. |
PDFMathTranslate: Scientific Document Translation Preserving Layouts (2025.emnlp-demos)
Copied to clipboard
| Challenge: | Prior efforts in translating scientific documents overlooked layouts . PDFMathTranslate is open-source with more than 222k downloads - a record for the first time ever. |
| Approach: | They propose PDFMathTranslate, the world's first open-source software for translating scientific documents while preserving layouts. |
| Outcome: | The work is open-sourced at https://github.com/byaidu/pdfmathtranslate with more than 222k downloads. |
Dataset Construction for Scientific-Document Writing Support by Extracting Related Work Section and Citations from PDF Papers (2022.lrec-1)
Copied to clipboard
| Challenge: | To augment datasets used for scientific-document writing support research, we extract texts from “Related Work” sections and citation information in PDF-formatted papers published in English. |
| Approach: | They propose to extract text from “Related Work” sections and citation information from PDF-formatted papers published in English. |
| Outcome: | The proposed dataset is based on a previously constructed dataset using only Tex papers and is compared with the existing one. |
Hierarchy Builder: Organizing Textual Spans into a Hierarchy to Facilitate Navigation (2023.acl-demo)
Copied to clipboard
| Challenge: | Information extraction systems produce hundreds to thousands of strings on a specific topic. |
| Approach: | They propose a method that allows users to consume a large collection of related textual strings in an exploratory mode. |
| Outcome: | The proposed method allows users to consume a large collection of related textual strings in an exploratory mode. |
SciREX: A Challenge Dataset for Document-Level Information Extraction (2020.acl-main)
Copied to clipboard
| Challenge: | Conventional datasets and methods for information extraction focus on within-sentence relations from general Newswire text. |
| Approach: | They propose a document-level IE dataset that integrates automatic and human annotations to annotate entities and document- level N-ary relation identification from scientific articles. |
| Outcome: | The proposed dataset extends state-of-the-art IE models to document-level IE. |
Trafilatura: A Web Scraping Library and Command-Line Tool for Text Discovery and Extraction (2021.acl-demo)
Copied to clipboard
| Challenge: | Existing tools for text extraction and web corpus construction are not enough to extract and pre-process web data to meet scientific expectations with respect to text quality. |
| Approach: | They propose a text discovery and extraction tool published under open-source license that allows for main text, comments and metadata extraction while also providing building blocks for web crawling tasks. |
| Outcome: | The proposed tool performs significantly better than other open-source solutions on real-world data and in external benchmarks. |
READoc: A Unified Benchmark for Realistic Document Structured Extraction (2025.findings-acl)
Copied to clipboard
Zichao Li, Aizier Abulaiti, Yaojie Lu, Xuanang Chen, Jia Zheng, Hongyu Lin, Xianpei Han, Shanshan Jiang, Bin Dong, Le Sun
| Challenge: | Document Structured Extraction (DSE) is a field of document structure analysis that aims to extract structured content from raw documents. |
| Approach: | They propose a benchmark to evaluate document structured extraction systems by converting unstructured PDFs into semantically rich Markdown. |
| Outcome: | The proposed benchmark is based on 3,576 diverse and real-world documents from arXiv, GitHub, and Zenodo. |
STAPI: An Automatic Scraper for Extracting Iterative Title-Text Structure from Web Documents (2022.lrec-1)
Copied to clipboard
| Challenge: | Formal documents are organized into sections of text, each with a title . but there is no corpus of web documents annotated with titles and prose texts . cnn.com's john mccarthy and daniel mclears are working on a new title-text dataset . |
| Approach: | They propose a first title-text dataset on web documents that incorporates a wide variety of domains to facilitate downstream training. |
| Outcome: | The proposed system outperforms baseline models in terms of title-text identification. |