DocSplit: A Comprehensive Benchmark Dataset and Evaluation Approach for Document Packet Recognition and Splitting
arXiv:2602.15958v1 Announce Type: new Abstract: Document understanding in real-world applications often requires processing heterogeneous, multi-page document packets containing multiple documents stitched together. Despite recent advances in visual document understanding, the fundamental task of document packet splitting, which involves separating a document packet into individual units, remains largely unaddressed. We present the first comprehensive benchmark dataset, DocSplit, along with novel evaluation metrics for assessing the document packet splitting capabilities of large language models. DocSplit comprises five datasets of varying complexity, covering diverse document types, layouts, and multimodal settings. We formalize the DocSplit task, which requires models to identify document boundaries, classify document types, and maintain correct page ordering within a document packet. The benchmark addresses real-world challenges, including out-of-order pages, interleaved documents,
arXiv:2602.15958v1 Announce Type: new Abstract: Document understanding in real-world applications often requires processing heterogeneous, multi-page document packets containing multiple documents stitched together. Despite recent advances in visual document understanding, the fundamental task of document packet splitting, which involves separating a document packet into individual units, remains largely unaddressed. We present the first comprehensive benchmark dataset, DocSplit, along with novel evaluation metrics for assessing the document packet splitting capabilities of large language models. DocSplit comprises five datasets of varying complexity, covering diverse document types, layouts, and multimodal settings. We formalize the DocSplit task, which requires models to identify document boundaries, classify document types, and maintain correct page ordering within a document packet. The benchmark addresses real-world challenges, including out-of-order pages, interleaved documents, and documents lacking clear demarcations. We conduct extensive experiments evaluating multimodal LLMs on our datasets, revealing significant performance gaps in current models' ability to handle complex document splitting tasks. The DocSplit benchmark datasets and proposed novel evaluation metrics provide a systematic framework for advancing document understanding capabilities essential for legal, financial, healthcare, and other document-intensive domains. We release the datasets to facilitate future research in document packet processing.
Executive Summary
The article 'DocSplit: A Comprehensive Benchmark Dataset and Evaluation Approach for Document Packet Recognition and Splitting' introduces a novel benchmark dataset, DocSplit, designed to address the critical yet under-researched task of document packet splitting. This task involves separating multi-page document packets into individual documents, a common challenge in real-world applications such as legal, financial, and healthcare domains. The authors present five diverse datasets covering various document types, layouts, and multimodal settings, along with new evaluation metrics to assess the performance of large language models (LLMs) in this task. The study highlights significant performance gaps in current models, emphasizing the need for improved document understanding capabilities. The release of the DocSplit datasets aims to facilitate future research and advancements in document packet processing.
Key Points
- ▸ Introduction of the first comprehensive benchmark dataset, DocSplit, for document packet splitting.
- ▸ Formalization of the DocSplit task, including document boundary identification, type classification, and page ordering.
- ▸ Extensive experiments revealing performance gaps in current LLMs for complex document splitting tasks.
- ▸ Release of datasets to foster research in document packet processing across various domains.
Merits
Comprehensive Dataset
The creation of the DocSplit dataset, which includes five diverse subsets, addresses a significant gap in the current literature by providing a standardized benchmark for evaluating document packet splitting.
Novel Evaluation Metrics
The introduction of new evaluation metrics offers a systematic framework for assessing the performance of LLMs in document packet splitting, enhancing the rigor and comparability of future research.
Real-World Relevance
The datasets cover a wide range of document types and layouts, making the findings highly relevant to practical applications in legal, financial, and healthcare domains.
Demerits
Limited Model Diversity
The study primarily focuses on evaluating multimodal LLMs, which may not fully capture the performance of other types of models that could be relevant to document packet splitting.
Complexity of Tasks
The tasks involved in document packet splitting, such as identifying boundaries and maintaining page order, are inherently complex, which may limit the generalizability of the findings to simpler or more straightforward document processing tasks.
Expert Commentary
The introduction of the DocSplit benchmark dataset represents a significant advancement in the field of document understanding, addressing a critical yet often overlooked task in document processing. The comprehensive nature of the dataset, coupled with the novel evaluation metrics, provides a robust framework for assessing the capabilities of large language models in handling complex document packet splitting tasks. The study's findings highlight the current limitations of existing models, underscoring the need for further research and development in this area. The practical implications of this work are substantial, particularly in sectors such as legal, financial, and healthcare, where efficient and accurate document processing is paramount. The release of the datasets is a commendable initiative that will undoubtedly foster collaboration and innovation in the field. However, it is essential to consider the broader implications of such technologies, including data privacy and security concerns, to ensure their ethical and responsible deployment. Overall, this study sets a new standard for evaluating document packet splitting and paves the way for future advancements in document understanding.
Recommendations
- ✓ Future research should explore the integration of diverse model architectures beyond multimodal LLMs to provide a more comprehensive evaluation of document packet splitting capabilities.
- ✓ The development of additional datasets that include simpler document processing tasks could help in understanding the scalability and generalizability of the findings to a broader range of applications.
