Jul 2026· Revolutionary Advances in Computing and Electronics: An International Journal· Vol 2, pp. 17-32· 0 citations· 7 references
TL;DR
This study provides an AI- Based document analyzer with a question-answer system that makes use of Natural Language Processing approaches that is affordable, scalable, and suitable for business, education, and research.
Abstract
The exponential growth of textual data, in the form of corporate documents, reports, and research papers, in the current digital era has increased the need for intelligent systems that can automatically comprehend documents. Analysing these documents by hand is ineffective and time-consuming. In order to extract valuable insights from documents, this study provides an AI- Based document analyzer with a question-answer system that makes use of Natural Language Processing approaches. Users can submit text or PDF files to the system, which then extracts content, generates succinct summaries, identifies keywords, and enables interactive question-answering. Python is used to build the architecture, which is then serverless deployed on Amazon Web Services (AWS) utilising Amazon S3 and Amazon EC2, and for the question-answering system, GEMINI is used. By reducing reading time and providing immediate access to pertinent information, the suggested solution increases productivity. It is affordable, scalable, and suitable for business, education, and research.
The design realization and evaluation of an Automated Summarization Tool (AST) is presented which is a document intelligence platform based on google gemini 2.5 flash that outperforms the strongest fine-tuned transformer baselines (PEGASUS, BART) by ~14 points and is clearly ahead of BERTSUM-ext (a strong transformer baseline), Pointer-Generator Network, TextRank.
K. Kumar, A. Amandeep, Dharmender Kumar et al.· International Journal of Inn...· 0 citations
This work presents DocMaster, a hierarchical structure-aware document analysis system that parses documents into hierarchical document trees preserving original layouts and constructs a structure-aware semantic index that enables accurate document filtering and in-depth analysis.
Ziqi Chen, Yingli Zhou, Fangyuan Zhang et al.· 0 citations
The rapid digitalization of higher education requires academic information services that are fast, integrated, and accessible. Fragmented service channels, high administrative workloads, and slow response times remain persistent operational challenges. This study develops an Intelligent Transformer-Based One-Gate System that centralizes academic services by integrating text classification, abstractive summarization, and chatbot modules. Using a Research and Development approach, the system was built from 1,000 academic documents and service queries collected from FAQs, academic regulations, guides, and digital service archives. The corpus was cleaned, tokenized, encoded, and divided into training, validation, and testing subsets. BERT was applied for document and query classification, while BART and PEGASUS were evaluated for abstractive summarization using ROUGE metrics. The chatbot was assessed through a Likert-scale user acceptance survey. The results of this research show that the BERT classifier achieved 88% accuracy and an F1-score of 0.875, PEGASUS outperformed BART with ROUGE-1 = 0.74, ROUGE-2 = 0.66, and ROUGE-L = 0.71, and the chatbot achieved an average user satisfaction score of 82%. The main contribution of this research is an integrated transformer-based one-gate architecture that combines document routing, academic document summarization, and conversational assistance in a single service platform, offering practical value for reducing fragmented academic information services and methodological value as a reference model for higher education NLP implementation.
Jaka Purnama, Yayuk Ike Meilani· IDEALIS : InDonEsiA journaL...· 0 citations
Users can use search engines such as Google and Yahoo! to search for documents on the World Wide Web. It takes
time, but the user of a search engine must go through each document to find an answer that is relevant to the question. The
Query Answering (QA) method reduces the amount of time spent searching for the exact answer to a question. The study of
question-answering systems is an important aspect of the field of information retrieval. The year 1960 saw the start of research
into question-answering systems, and since then, a plethora of different question-answering systems have been developed. The
Question Answering system combines research from several fields, including Natural Language Processing, Artificial
Intelligence, Information Retrieval, and Information Extraction. The goal of a question answering system is to provide a precise
response in natural language to the user's question. The availability of various resources for responses is used to differentiate
between different types of question answering systems. In comparison to the open domain question answering system, the closed
domain question answering system provides more precise and accurate responses.
S. Sayyed, Bharat Shelke, C. Mahender· International Journal for Re...· 0 citations
Due to the surge in digital document creation, intelligent systems that can effectively retrieve accurate information from unstructured PDF content have become a necessity. Current keyword-based search techniques lack semantic meaning, while large language models (LLMs), when utilized individually, produce hallucinated results when they are not contextually aware of document content. In this paper, we introduce a novel real-time question answering system for PDF content using a lightweight approach to Retrieval-Augmented Generation (RAG). Our system combines a HuggingFace sentence transformer model for semantic embeddings with a FAISS vector similarity search for efficient retrieval, leveraging a Groq-hosted LLaMA 3.3 70B model for context-grounded answer generation. The system is built using LangChain for orchestration and Streamlit for web-based deployment, and requires no GPU, no fine-tuning, and no pre-indexed corpus. Evaluation across five complementary metrics—Exact Match, Token-level F1, BLEU, ROUGE, and semantic similarity—demonstrates strong alignment between generated and reference answers, with a peak semantic similarity of 0.8709 and a mean Token F1 of 0.4701, confirming the viability of this approach for real-world document intelligence.
Mukesh Lakshmi Sai Medikonda, Kalva Vishnu Teja, K. Greeshma et al.· International Conference Com...· 0 citations
Providing appropriate answers to questions is necessary in many situations, not just in the conversational AI systems we see and develop today. Research in Natural Language Processing (NLP) and User Modeling (UM) have investigated this topic for decades, starting in the era of ''symbolic AI''. While NLP in general was needed for the whole interaction (understanding the question and answering it), Natural Language Generation (NLG) was particularly concerned with providing good and coherent answers appropriate for the information need and the intended audience, which is where UM also played a role. At that time, information to include in the answers typically came from knowledge bases or data bases. Information Retrieval (IR) then was concerned with retrieving the documents (and later websites) most relevant to a query. As the amount of data and number of documents increased, information needs from users became increasingly complex. As a result, it seemed that combining advances in both NLP and IR was required. And of course, now, research often spans these two fields. In this talk, I will look at past work in the fields of NLG and UM, outlining what was identified as important for graceful human machine interactions. The game has changed now, of course, with LLMs and generative AI, which can do much that we could not do before. But some old questions remain unanswered, there are new questions (especially given the ''black box'' nature of LLMs), and we can probably learn from some earlier work. I will also discuss future research directions that I believe are important.
Cécile Paris· Annual International ACM SIG...· 0 citations