ChatMyDoc: AI Powered Document Interaction Assistant

Abstract

The exponential growth of digital data has increased the need for intelligent systems that simplify document comprehension and accelerate information retrieval. Manual reading and analysis of lengthy documents remain cognitively demanding and time-consuming, particularly in academia, research, and professional workflows. This review paper examines existing artificial intelligence based solutions in document processing, including text summarization, natural language querying, annotation systems, and knowledge visualization techniques. Inspired by limitations identified in current technologies, this paper introduces ChatMyDoc, a natural language driven document interaction assistant designed to enhance user understanding and efficiency. Built using modern web technologies integrated with advanced AI models, the system performs PDF querying, abstractive and extractive summarization, annotation storage, and automated flowchart generation. The design focuses on reducing cognitive load, improving accessibility to information, and enabling intuitive engagement with digital content. The review highlights comparative analysis of related methodologies, identifies research gaps, and positions ChatMyDoc as a multifunctional, domain-independent document intelligence solution suitable for education, research, and enterprise document management.

Introduction

The rapid digitalization of academic and industrial workflows has led to an overwhelming amount of textual information, requiring effective methods for processing, understanding, and summarizing digital documents. Traditional reading and annotation methods are inefficient, often causing cognitive overload and missed insights. Advances in Natural Language Processing (NLP) and transformer-based models now allow for automated summarization, contextual retrieval, and semantic understanding of text. However, most current systems perform only isolated tasks such as summarization or note extraction and lack integrated, interactive platforms for deeper document comprehension.

Purpose and Contribution

This review examines recent developments in text summarization and intelligent document interaction tools, identifying their limitations and motivating the creation of ChatMyDoc — a proposed AI-assisted multifunctional system designed for summarization, querying, annotation, and visualization. The goal is to make document interaction more efficient, interactive, and learning-oriented.

Literature Review

Research shows that digital users in academia, healthcare, and corporate sectors rely heavily on lengthy documents like PDFs and reports. Traditional tools allow only static reading, limiting productivity. Modern AI systems integrate summarization, semantic search, and annotation, transforming raw text into structured knowledge.

Summarization techniques have evolved:

• Early methods: Frequency-based and rule-based extractive models.

• Intermediate models: Optimization and clustering approaches.

• Modern models: Transformer-based architectures capturing deeper semantic meaning.

Despite this progress, studies (e.g., Azam et al., 2024) highlight persistent issues such as contextual inaccuracy in long documents, lack of interactive querying, and absence of visual learning support.

Four key functional areas were identified in existing intelligent document systems:

1. Document Preprocessing and Parsing – text extraction from complex layouts.

2. Summarization Techniques – mostly extractive, limited abstractive generation.

3. Semantic Query Retrieval – transformer-based similarity for contextual Q&A.

4. Annotation and Visualization – minimal tools for visual learning.

Overall, existing systems demonstrate strong summarization performance but lack interactivity, personalization, and visualization — essential for effective learning and knowledge retention.

Methodology

The review used a systematic research approach, selecting 10 papers (2019–2025) from databases like IEEE Xplore and Google Scholar. Keywords included text summarization, semantic information retrieval, and AI document assistant. Studies were compared based on methodologies, performance metrics (ROUGE, BLEU), and interactivity levels. This process ensured objective analysis and identification of key technological gaps addressed by ChatMyDoc.

Findings and Discussion

• Technological Progress: There is a clear evolution from rule-based and statistical summarizers to neural and embedding-based models that provide higher contextual accuracy.

• Persistent Limitations:

  1. Static summaries with no interactive or conversational exploration.

  2. Lack of visual support for understanding complex information.

  3. High computational costs of advanced deep learning models.

  4. Limited personalization and integration with user learning processes.

These insights highlight the necessity for next-generation document comprehension tools that merge summarization, semantic querying, annotation, and visualization into one adaptive platform — a gap that the ChatMyDoc system seeks to fill.

Conclusion

The rapid expansion of digital document usage has increased the need for systems that simplify information retrieval and enhance comprehension. Existing solutions for text summarization often focus on individual capabilities such as extraction or clustering, offering limited interactivity and minimal contextual understanding. Additionally, most systems do not provide adaptive assistance that supports actual user workflows such as asking questions, annotating content, or visualizing contextual knowledge. This review has examined current advancements in document summarization, emphasizing the rise of transformer-based semantic models and optimization-driven extractive techniques. The evaluation highlights clear gaps in accessibility, real-time interaction, and cognitive support for users. ChatMyDoc has been conceptually positioned as an enhanced intelligent assistant that addresses these gaps by integrating extractive summarization, semantic querying, annotation support, and automated visual generation into a unified platform. The reviewed literature and identified research challenges provide strong evidence that future document-interaction systems must evolve beyond static summarization toward dynamic and personalized knowledge retrieval that improves usability and learning efficiency.

References

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Copyright

Copyright © 2025 Shreeraj N. Landge, Prof. P. V. Kulkarni, Khushi M., Shreyas Chafakanade. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.