BoundingDocs: a Unified Dataset for Document Question Answering with Spatial Annotations

• URL: http://arxiv.org/abs/2501.03403v1
• Date: Mon, 06 Jan 2025 21:46:22 GMT
• Title: BoundingDocs: a Unified Dataset for Document Question Answering with Spatial Annotations
• Authors: Simone Giovannini, Fabio Coppini, Andrea Gemelli, Simone Marinai,
• Abstract summary: We present a unified dataset for document Question-Answering (QA) We reformulate existing Document AI tasks, such as Information Extraction (IE), into a Question-Answering task. On the other hand, we release the OCR of all the documents and include the exact position of the answer to be found in the document image as a bounding box.
• Score: 2.9798896492745537
• License:
• Abstract: We present a unified dataset for document Question-Answering (QA), which is obtained combining several public datasets related to Document AI and visually rich document understanding (VRDU). Our main contribution is twofold: on the one hand we reformulate existing Document AI tasks, such as Information Extraction (IE), into a Question-Answering task, making it a suitable resource for training and evaluating Large Language Models; on the other hand, we release the OCR of all the documents and include the exact position of the answer to be found in the document image as a bounding box. Using this dataset, we explore the impact of different prompting techniques (that might include bounding box information) on the performance of open-weight models, identifying the most effective approaches for document comprehension.

Related papers

• DocKD: Knowledge Distillation from LLMs for Open-World Document Understanding Models [66.91204604417912]
  This study aims to enhance generalizability of small VDU models by distilling knowledge from LLMs. We present a new framework (called DocKD) that enriches the data generation process by integrating external document knowledge. Experiments show that DocKD produces high-quality document annotations and surpasses the direct knowledge distillation approach.
  arXiv  Detail & Related papers  (2024-10-04T00:53:32Z)
• Unified Multimodal Interleaved Document Representation for Retrieval [57.65409208879344]
  We propose a method that holistically embeds documents interleaved with multiple modalities. We merge the representations of segmented passages into one single document representation. We show that our approach substantially outperforms relevant baselines.
  arXiv  Detail & Related papers  (2024-10-03T17:49:09Z)
• BuDDIE: A Business Document Dataset for Multi-task Information Extraction [18.440587946049845]
  BuDDIE is the first multi-task dataset of 1,665 real-world business documents. Our dataset consists of publicly available business entity documents from US state government websites.
  arXiv  Detail & Related papers  (2024-04-05T10:26:42Z)
• Open Domain Multi-document Summarization: A Comprehensive Study of Model Brittleness under Retrieval [42.73076855699184]
  Multi-document summarization (MDS) assumes a set of topic-related documents are provided as input. We study this more challenging setting by formalizing the task and bootstrapping it using existing datasets, retrievers and summarizers.
  arXiv  Detail & Related papers  (2022-12-20T18:41:38Z)
• Unifying Vision, Text, and Layout for Universal Document Processing [105.36490575974028]
  We propose a Document AI model which unifies text, image, and layout modalities together with varied task formats, including document understanding and generation. Our method sets the state-of-the-art on 9 Document AI tasks, e.g., document understanding and QA, across diverse data domains like finance reports, academic papers, and websites.
  arXiv  Detail & Related papers  (2022-12-05T22:14:49Z)
• Layout-Aware Information Extraction for Document-Grounded Dialogue: Dataset, Method and Demonstration [75.47708732473586]
  We propose a layout-aware document-level Information Extraction dataset, LIE, to facilitate the study of extracting both structural and semantic knowledge from visually rich documents. LIE contains 62k annotations of three extraction tasks from 4,061 pages in product and official documents. Empirical results show that layout is critical for VRD-based extraction, and system demonstration also verifies that the extracted knowledge can help locate the answers that users care about.
  arXiv  Detail & Related papers  (2022-07-14T07:59:45Z)
• Unified Pretraining Framework for Document Understanding [52.224359498792836]
  We present UDoc, a new unified pretraining framework for document understanding. UDoc is designed to support most document understanding tasks, extending the Transformer to take multimodal embeddings as input. An important feature of UDoc is that it learns a generic representation by making use of three self-supervised losses.
  arXiv  Detail & Related papers  (2022-04-22T21:47:04Z)
• Evaluation of a Region Proposal Architecture for Multi-task Document Layout Analysis [0.685316573653194]
  Mask-RCNN architecture is designed to address the problem of baseline detection and region segmentation. We present experimental results on two handwritten text datasets and one handwritten music dataset. The analyzed architecture yields promising results, outperforming state-of-the-art techniques in all three datasets.
  arXiv  Detail & Related papers  (2021-06-22T14:07:27Z)
• AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization [17.098075160558576]
  We propose a scalable approach called AQuaMuSe to automatically mine qMDS examples from question answering datasets and large document corpora. We publicly release a specific instance of an AQuaMuSe dataset with 5,519 query-based summaries, each associated with an average of 6 input documents selected from an index of 355M documents from Common Crawl.
  arXiv  Detail & Related papers  (2020-10-23T22:38:18Z)
• SciREX: A Challenge Dataset for Document-Level Information Extraction [56.83748634747753]
  It is challenging to create a large-scale information extraction dataset at the document level. We introduce SciREX, a document level IE dataset that encompasses multiple IE tasks. We develop a neural model as a strong baseline that extends previous state-of-the-art IE models to document-level IE.
  arXiv  Detail & Related papers  (2020-05-01T17:30:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.