RealitySummary: On-Demand Mixed Reality Document Enhancement using Large Language Models

• URL: http://arxiv.org/abs/2405.18620v1
• Date: Tue, 28 May 2024 21:59:56 GMT
• Title: RealitySummary: On-Demand Mixed Reality Document Enhancement using Large Language Models
• Authors: Aditya Gunturu, Shivesh Jadon, Nandi Zhang, Jarin Thundathil, Wesley Willett, Ryo Suzuki,
• Abstract summary: We introduce RealitySummary, a mixed reality reading assistant that can enhance any printed or digital document using on-demand text extraction, summarization, and augmentation. We demonstrate real-time examples of six specific document augmentations: 1) summaries, 2) comparison tables, 3) timelines, 4) keyword lists, 5) summary highlighting, and 6) information cards.
• Score: 13.906648004819107
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce RealitySummary, a mixed reality reading assistant that can enhance any printed or digital document using on-demand text extraction, summarization, and augmentation. While augmented reading tools promise to enhance physical reading experiences with overlaid digital content, prior systems have typically required pre-processed documents, which limits their generalizability and real-world use cases. In this paper, we explore on-demand document augmentation by leveraging large language models. To understand generalizable techniques for diverse documents, we first conducted an exploratory design study which identified five categories of document enhancements (summarization, augmentation, navigation, comparison, and extraction). Based on this, we developed a proof-of-concept system that can automatically extract and summarize text using Google Cloud OCR and GPT-4, then embed information around documents using a Microsoft Hololens 2 and Apple Vision Pro. We demonstrate real-time examples of six specific document augmentations: 1) summaries, 2) comparison tables, 3) timelines, 4) keyword lists, 5) summary highlighting, and 6) information cards. Results from a usability study (N=12) and in-the-wild study (N=11) highlight the potential benefits of on-demand MR document enhancement and opportunities for future research.

Related papers

• DocGenome: An Open Large-scale Scientific Document Benchmark for Training and Testing Multi-modal Large Language Models [63.466265039007816]
  We present DocGenome, a structured document benchmark constructed by annotating 500K scientific documents from 153 disciplines in the arXiv open-access community. We conduct extensive experiments to demonstrate the advantages of DocGenome and objectively evaluate the performance of large models on our benchmark.
  arXiv  Detail & Related papers  (2024-06-17T15:13:52Z)
• Functional Analytics for Document Ordering for Curriculum Development and Comprehension [0.0]
  We propose techniques for automatic document order generation for curriculum development and for creation of optimal reading order for use in learning, training, and other content-sequencing applications. Such techniques could potentially be used to improve comprehension, identify areas that need expounding, generate curricula, and improve search engine results.
  arXiv  Detail & Related papers  (2023-11-22T02:13:27Z)
• DocPedia: Unleashing the Power of Large Multimodal Model in the Frequency Domain for Versatile Document Understanding [98.41782470335032]
  This work presents DocPedia, a novel large multimodal model (LMM) for versatile OCR-free document understanding. Unlike existing work either struggle with high-resolution documents or give up the large language model thus vision or language ability constrained, our DocPedia directly processes visual input in the frequency domain rather than the pixel space.
  arXiv  Detail & Related papers  (2023-11-20T14:42:25Z)
• Augmented Math: Authoring AR-Based Explorable Explanations by Augmenting Static Math Textbooks [1.8097223019080158]
  We introduce Augmented Math, a machine learning-based approach to authoring AR explorable explanations by augmenting static math textbooks without programming. To augment a static document, our system first extracts mathematical formulas and figures from a given document using optical character recognition (OCR) and computer vision. This empowers non-technical users, such as teachers or students, to transform existing math textbooks and handouts into on-demand and personalized explorable explanations.
  arXiv  Detail & Related papers  (2023-07-30T03:02:52Z)
• Generate rather than Retrieve: Large Language Models are Strong Context Generators [74.87021992611672]
  We present a novel perspective for solving knowledge-intensive tasks by replacing document retrievers with large language model generators. We call our method generate-then-read (GenRead), which first prompts a large language model to generate contextutal documents based on a given question, and then reads the generated documents to produce the final answer.
  arXiv  Detail & Related papers  (2022-09-21T01:30:59Z)
• TRIE++: Towards End-to-End Information Extraction from Visually Rich Documents [51.744527199305445]
  This paper proposes a unified end-to-end information extraction framework from visually rich documents. Text reading and information extraction can reinforce each other via a well-designed multi-modal context block. The framework can be trained in an end-to-end trainable manner, achieving global optimization.
  arXiv  Detail & Related papers  (2022-07-14T08:52:07Z)
• 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)
• Enhancing Extractive Text Summarization with Topic-Aware Graph Neural Networks [21.379555672973975]
  This paper proposes a graph neural network (GNN)-based extractive summarization model. Our model integrates a joint neural topic model (NTM) to discover latent topics, which can provide document-level features for sentence selection. The experimental results demonstrate that our model achieves substantially state-of-the-art results on CNN/DM and NYT datasets.
  arXiv  Detail & Related papers  (2020-10-13T09:30:04Z)
• From Standard Summarization to New Tasks and Beyond: Summarization with Manifold Information [77.89755281215079]
  Text summarization is the research area aiming at creating a short and condensed version of the original document. In real-world applications, most of the data is not in a plain text format. This paper focuses on the survey of these new summarization tasks and approaches in the real-world application.
  arXiv  Detail & Related papers  (2020-05-10T14:59:36Z)

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.