insight - Document Understanding - # Realistic Document Structured Extraction Benchmark

READOC: A Unified Benchmark for Realistic and Comprehensive Document Structured Extraction

Q: How can the READOC benchmark be extended to support other document formats beyond PDF, such as Word or HTML, to further broaden its applicability?

To extend the READOC benchmark for other document formats like Word or HTML, several strategies can be employed. First, a comprehensive document collection process should be established for these formats, similar to the existing methodology for PDFs. This would involve selecting a diverse range of documents from repositories like Microsoft Word's online resources or HTML-based content from websites. Next, a conversion pipeline must be developed to transform these formats into a standardized Markdown output. For Word documents, tools like Pandoc can be utilized to convert .docx files into Markdown, ensuring that structural elements such as headings, lists, and tables are accurately represented. For HTML documents, a similar approach can be taken, where the HTML structure is parsed and converted into Markdown while preserving semantic meaning. Additionally, the evaluation S3uite of READOC should be adapted to accommodate the unique characteristics of these formats. This includes developing new standardization, segmentation, and scoring modules that can handle the specific layout and content structures found in Word and HTML documents. By incorporating these formats, READOC can provide a more comprehensive benchmark for Document Structured Extraction (DSE) systems, facilitating the evaluation of models across a wider array of real-world document types.

Q: What are the potential challenges in developing DSE systems that can effectively handle the long-range dependencies and complex logical structures present in realistic multi-page documents?

Developing DSE systems capable of managing long-range dependencies and complex logical structures in multi-page documents presents several challenges. One significant challenge is the inherent complexity of multi-page layouts, which often include hierarchical headings, footnotes, and references that span across pages. This necessitates models that can maintain context and coherence over extended text segments, requiring advanced memory mechanisms or attention-based architectures that can effectively capture relationships between distant elements. Another challenge lies in the variability of document structures. Real-world documents can exhibit diverse formatting styles, such as varying heading levels, multi-column layouts, and embedded figures or tables. DSE systems must be robust enough to adapt to these variations while accurately extracting and structuring information. This requires extensive training on diverse datasets to ensure models generalize well across different document types. Additionally, the integration of visual information, such as images and graphs, adds another layer of complexity. DSE systems must not only extract textual content but also interpret and represent visual elements in a structured format. This necessitates the development of multimodal models that can process both text and images simultaneously, further complicating the design and training processes.

Q: How can the READOC benchmark be leveraged to drive the development of more efficient and scalable DSE models that can meet the real-world requirements of large-scale knowledge base construction and retrieval-augmented generation tasks?

The READOC benchmark can significantly influence the development of efficient and scalable DSE models by providing a standardized framework for evaluation and comparison. By establishing clear metrics and evaluation criteria through its S3uite, researchers can identify specific areas where existing models fall short, such as in handling complex document structures or maintaining reading order across multiple pages. Moreover, the diverse dataset of 2,233 documents in READOC allows for the training of models on a wide range of document types and structures, promoting the development of more generalized DSE systems. This diversity can help models learn to handle various layouts and content types, which is crucial for real-world applications in knowledge base construction and retrieval-augmented generation tasks. Additionally, the benchmark can serve as a catalyst for innovation by encouraging the exploration of new modeling paradigms, such as end-to-end architectures that integrate multiple DSE capabilities into a single framework. By highlighting the gaps in current research, READOC can inspire the development of novel approaches that prioritize efficiency and scalability, ultimately leading to DSE systems that can process large volumes of documents quickly and accurately. In summary, leveraging the READOC benchmark can drive advancements in DSE by fostering a competitive research environment, promoting the development of robust models, and ensuring that these models meet the practical demands of large-scale document processing tasks.

Conceitos essenciais

READOC is a novel benchmark that frames document structured extraction as a realistic, end-to-end task of converting unstructured PDFs into semantically rich Markdown text, enabling a unified evaluation of state-of-the-art approaches.

Resumo

The paper introduces READOC, a unified benchmark for realistic and comprehensive document structured extraction (DSE). READOC frames DSE as a task of converting unstructured PDF documents into structured Markdown text, addressing the limitations of existing fragmented and localized benchmark paradigms.

The key highlights of the paper are:

READOC dataset construction: The benchmark is constructed from 2,233 diverse real-world documents from arXiv and GitHub, covering a wide range of types, topics, and layouts.
READOC Evaluation S3uite: The authors develop a three-module evaluation suite to enable a unified assessment of DSE systems, including Standardization, Segmentation, and Scoring.
Experimental evaluation: The authors evaluate a range of DSE systems, including pipeline tools, expert visual models, and general vision-language models, revealing critical gaps between current research and the realistic DSE objective.
Fine-grained analysis: The paper examines the impact of document length, depth, and layout complexity on DSE system performance, highlighting the need for new modeling paradigms to handle realistic multi-page documents.
Efficiency considerations: The authors compare the throughput of different DSE systems, indicating the need for future improvements in both performance and efficiency.

The paper aspires that READOC will catalyze future research in DSE, fostering more comprehensive and practical solutions.

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Estatísticas

The index δ is 1/3.
READOC-arXiv contains 1,009 documents with an average of 11.67 pages, 10,209.50 tokens, and 3.10 heading levels.
READOC-GitHub contains 1,224 documents with an average of 6.54 pages, 1,978.11 tokens, and 3.11 heading levels.

Citações

"READOC is the first benchmark to frame DSE as a PDF-to-Markdown paradigm, which is realistic, end-to-end, and incorporates diverse data."
"An evaluation S3uite is proposed to support the unified assessment of various DSE systems and to quantify multiple capabilities required for DSE."
"We present the gap between current research and realistic DSE, emphasizing the importance of exploring new modeling paradigms."

Principais Insights Extraídos De

READoc: A Unified Benchmark for Realistic Document Structured Extraction

by Zichao Li, A... às arxiv.org 09-10-2024

https://arxiv.org/pdf/2409.05137.pdf

READoc: A Unified Benchmark for Realistic Document Structured Extraction

Perguntas Mais Profundas

How can the READOC benchmark be extended to support other document formats beyond PDF, such as Word or HTML, to further broaden its applicability?

To extend the READOC benchmark for other document formats like Word or HTML, several strategies can be employed. First, a comprehensive document collection process should be established for these formats, similar to the existing methodology for PDFs. This would involve selecting a diverse range of documents from repositories like Microsoft Word's online resources or HTML-based content from websites.
Next, a conversion pipeline must be developed to transform these formats into a standardized Markdown output. For Word documents, tools like Pandoc can be utilized to convert .docx files into Markdown, ensuring that structural elements such as headings, lists, and tables are accurately represented. For HTML documents, a similar approach can be taken, where the HTML structure is parsed and converted into Markdown while preserving semantic meaning.
Additionally, the evaluation S3uite of READOC should be adapted to accommodate the unique characteristics of these formats. This includes developing new standardization, segmentation, and scoring modules that can handle the specific layout and content structures found in Word and HTML documents. By incorporating these formats, READOC can provide a more comprehensive benchmark for Document Structured Extraction (DSE) systems, facilitating the evaluation of models across a wider array of real-world document types.

What are the potential challenges in developing DSE systems that can effectively handle the long-range dependencies and complex logical structures present in realistic multi-page documents?

Developing DSE systems capable of managing long-range dependencies and complex logical structures in multi-page documents presents several challenges. One significant challenge is the inherent complexity of multi-page layouts, which often include hierarchical headings, footnotes, and references that span across pages. This necessitates models that can maintain context and coherence over extended text segments, requiring advanced memory mechanisms or attention-based architectures that can effectively capture relationships between distant elements.
Another challenge lies in the variability of document structures. Real-world documents can exhibit diverse formatting styles, such as varying heading levels, multi-column layouts, and embedded figures or tables. DSE systems must be robust enough to adapt to these variations while accurately extracting and structuring information. This requires extensive training on diverse datasets to ensure models generalize well across different document types.
Additionally, the integration of visual information, such as images and graphs, adds another layer of complexity. DSE systems must not only extract textual content but also interpret and represent visual elements in a structured format. This necessitates the development of multimodal models that can process both text and images simultaneously, further complicating the design and training processes.

How can the READOC benchmark be leveraged to drive the development of more efficient and scalable DSE models that can meet the real-world requirements of large-scale knowledge base construction and retrieval-augmented generation tasks?

The READOC benchmark can significantly influence the development of efficient and scalable DSE models by providing a standardized framework for evaluation and comparison. By establishing clear metrics and evaluation criteria through its S3uite, researchers can identify specific areas where existing models fall short, such as in handling complex document structures or maintaining reading order across multiple pages.
Moreover, the diverse dataset of 2,233 documents in READOC allows for the training of models on a wide range of document types and structures, promoting the development of more generalized DSE systems. This diversity can help models learn to handle various layouts and content types, which is crucial for real-world applications in knowledge base construction and retrieval-augmented generation tasks.
Additionally, the benchmark can serve as a catalyst for innovation by encouraging the exploration of new modeling paradigms, such as end-to-end architectures that integrate multiple DSE capabilities into a single framework. By highlighting the gaps in current research, READOC can inspire the development of novel approaches that prioritize efficiency and scalability, ultimately leading to DSE systems that can process large volumes of documents quickly and accurately.
In summary, leveraging the READOC benchmark can drive advancements in DSE by fostering a competitive research environment, promoting the development of robust models, and ensuring that these models meet the practical demands of large-scale document processing tasks.