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This paper introduces AI-Powered Content Extractor, a novel command-line tool designed to extract and summarize content from PDF documents and web pages using advanced AI agent technology. We demonstrate how this tool addresses the growing challenge of information overload by providing efficient, customizable summarization capabilities across multiple sources. Our evaluations show that the tool achieves significant time savings while maintaining high summary quality across various document types. The open-source implementation provides a flexible framework that can be adapted for diverse use cases in research, business intelligence, knowledge management and data collection.
In the digital age, professionals and researchers face an overwhelming volume of information distributed across various formats. The ability to efficiently extract, process, and distill key information from multiple sources has become a critical skill. Traditional methods of manual reading and summarization are time-consuming and struggle to scale with the exponential growth of digital content.
This paper presents AI-Powered Content Extractor, a command-line tool that leverages recent advances in artificial intelligence to automate the extraction and summarization of content from PDF documents and web pages. The tool aims to bridge the gap between the abundance of available information and the limited time users have to process it, providing a practical solution for knowledge workers across various domains.
The key contributions of this work include:
Automated text summarization has a rich history in natural language processing research. Early approaches relied on statistical methods to identify important sentences or keywords (Luhn, 1958; Edmundson, 1969). These extractive summarization techniques evolved to incorporate graph-based algorithms (Mihalcea & Tarau, 2004) and machine learning methods (Wong et al., 2008).
With the advent of deep learning, neural network-based approaches have dominated the field. Sequence-to-sequence models with attention mechanisms (Rush et al., 2015; See et al., 2017) and transformer-based architectures (Liu & Lapata, 2019) have pushed the state-of-the-art in abstractive summarization, enabling more coherent and human-like summaries.
Several existing tools address document summarization, including commercial solutions like Quillbot and academic systems such as SUMMA (Liepins et al., 2017). However, these typically focus on specific document types or lack the flexibility of a command-line interface suitable for integration into complex workflows. Tools like Allen AI's SCIBERT (Beltagy et al., 2019) provide specialized capabilities for scientific documents but require significant technical expertise to implement.
Our work builds upon these advances while addressing the practical needs of end-users through an accessible, versatile command-line tool that supports multiple document formats and summarization styles.
AI-Powered Content Extractor employs a modular architecture consisting of three main components:
Content Extraction Module: Handles the parsing of different document formats (PDF and web pages) and converts them into a standardized text representation.
AI Processing Engine: Interfaces with AI models through APIs to generate summaries based on the extracted content.
Command-Line Interface: Provides user interaction, configuration management, and output formatting.
For PDF documents, we utilize a combination of PyPDF2 and pdf2text libraries to extract text content while preserving structural elements such as headings, paragraphs, and lists. The extraction process includes heuristics to handle multi-column layouts, embedded images, and tables.
For web pages, we employ BeautifulSoup and Requests libraries to retrieve and parse HTML content. Our approach includes intelligent content detection to differentiate between main article content and peripheral elements such as navigation menus, advertisements, and comments.
The summarization process leverages state-of-the-art language models through their respective APIs. The system is designed to be model-agnostic, allowing users to configure their preferred AI service and model. The default configuration uses models similar to GPT architecture due to their demonstrated capability in generating coherent and context-aware summaries.
We implement four distinct summarization styles to accommodate different user needs via using extra prompt customization:
The tool demonstrated strong performance across all document types, with particularly high scores for news articles and technical documentation. Academic papers presented the greatest challenge, likely due to their specialized terminology and complex structure. The treu power of this is system is the behind of agent technology. This makes system for flexible, true and fast.
The experimental results demonstrate that AI-Powered Content Extractor effectively balances efficiency and quality. The tool performs particularly well with web-based content due to the structured nature of HTML documents. PDF processing presents more challenges, especially with documents containing complex formatting, scanned text, or non-standard layouts.
The variation in performance across different document types highlights the importance of the content extraction phase. Our analysis suggests that improvements in this phase would yield significant gains in overall system performance, particularly for academic papers and documents with non-standard formatting.
We identified several compelling use cases through user feedback:
Research Literature Review: Researchers used the tool to process large volumes of academic papers during literature reviews, reducing the initial screening time by approximately 70%.
Competitive Intelligence: Business analysts employed the batch processing feature to monitor competitor websites and product documentation, enabling more comprehensive market analysis.
Knowledge Management: Technical teams used the tool to create searchable archives of summarized documentation, improving information retrieval and knowledge sharing.
Data Extractor: With the extensive and customizable promt interaction while extracting informations. We can extract and format data for data collection.
Despite its effectiveness, the system has several limitations:
API Dependency: Reliance on external AI APIs introduces potential issues with cost, rate limits, and service availability.
Visual Content: The tool cannot effectively summarize information contained in images, charts, or diagrams.
Domain Specificity: Highly specialized content may require domain-adapted models for optimal performance.
AI-Powered Content Extractor provides a practical solution to the challenge of information overload by enabling efficient extraction and summarization of content from PDF documents and web pages. Our evaluations demonstrate that the tool achieves significant time savings while maintaining high summary quality across various document types.
The open-source implementation offers a flexible framework that can be extended to support additional document formats, summarization styles, and AI models. Future work will focus on enhancing the content extraction capabilities for complex documents, improving multilingual support, and developing methods to incorporate visual content into the summarization process.
By bridging the gap between advanced AI capabilities and practical user needs, this tool contributes to making information processing more efficient and accessible for researchers, knowledge workers, and decision-makers.
Beltagy, I., Lo, K., & Cohan, A. (2019). SciBERT: A pretrained language model for scientific text. In Proceedings of EMNLP-IJCNLP 2019.
Edmundson, H. P. (1969). New methods in automatic extracting. Journal of the ACM, 16(2), 264-285.
Liepins, R., Germann, U., Barzdins, G., Birch, A., Renals, S., Weber, S., ... & Pinnis, M. (2017). The SUMMA platform: A scalable infrastructure for multi-lingual multi-media monitoring. In Proceedings of ACL 2017, System Demonstrations.
Liu, Y., & Lapata, M. (2019). Text summarization with pretrained encoders. In Proceedings of EMNLP-IJCNLP 2019.
Luhn, H. P. (1958). The automatic creation of literature abstracts. IBM Journal of Research and Development, 2(2), 159-165.
Mihalcea, R., & Tarau, P. (2004). TextRank: Bringing order into text. In Proceedings of EMNLP 2004.
Rush, A. M., Chopra, S., & Weston, J. (2015). A neural attention model for abstractive sentence summarization. In Proceedings of EMNLP 2015.
See, A., Liu, P. J., & Manning, C. D. (2017). Get to the point: Summarization with pointer-generator networks. In Proceedings of ACL 2017.
Wong, K. F., Wu, M., & Li, W. (2008). Extractive summarization using supervised and semi-supervised learning. In Proceedings of COLING 2008.
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