Jul 2026· International Journal of Innovative Research in Computer and Communication Engineering· 0 citations
TL;DR
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.
Abstract
As the size of file is increasing in non-structured text format in academic, corporate, law and organisation, there is a severe need of information-extraction and text-summarization systems. Doing summary manually is a time consuming, inconsistent and unscalable process. This paper presents the design realization and evaluation of an Automated Summarization Tool (AST) which is a document intelligence platform based on google gemini 2.5 flash. The platform employs map-reduce summarization for long documents, the use of SHA-256 hash for caching, a RAG-lite chat module grounded in the source document enabling conversational chats, role- and toneadaptive prompt engineering, and a JSON-based output schema coupling every extracted key point with a verbatim quote and location from the source for traceability, thereby preventing unnecessary API calls. The complete system features a Gradio web interface. It is deployed as a zero-infrastructure Google Colab notebook. Thus, no dedicated server or installation is required. In internal, multi-domain test corpora, our platform achieves a ROUGE-1 score of 58.17. This is the highest we obtain out of six different summarization systems with which we compare against other methods. And it outperforms the strongest fine-tuned transformer baselines (PEGASUS, BART) by ~14 points. And it is clearly ahead of BERTSUM-ext (a strong transformer baseline), Pointer-Generator Network, TextRank.
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.
Radhika Sharma, Devraj Gautam· Revolutionary Advances in Co...· 0 citations
The fast expansion of digital data has resulted in high demand for smart systems which can quickly obtain
compact and meaningful data from lengthy documents. Even though automation in text summarization has seen
significant developments in well-resourced languages, automated summarization of Kannada is still rare due to the
intricacies of the script of the language, poor computational power, and massive information available in printed and
scanned form. A web-based framework for automated summarization of Kannada documents is introduced in this paper,
utilizing AI by developing a processing platform that integrates OCR and NLP. This technique works not only with
originally typed text in Kannada but also with scanned documents. Scanned documents are converted to editable Unicode
by using Tesseract OCR engine before performing the language-specific NLP tasks that include normalization,
tokenization, sentence splitting, stopword removal, and extraction of summaries. Using technologies such as Python, Flask,
OpenCV, Tesseract OCR, and relational database management, the developed application can ensure secure
authentication of users, management of documents, and visualization of summaries using an interactive web interface. The
successful tests indicate that OCR and NLP technologies have been integrated into the process of performing various tasks
related to the examination of documents written in the Kannada language. The modular architecture of the project
enables applying transformer-based summaries, document processing in many languages, OCR of handwritten texts
written in the Kannada language, and various technologies for running applications in the cloud in the future. Thus, the
developed application is an example of the effective use of Artificial Intelligence in processing documents in regional
languages and lays the groundwork for creating automated systems for document management.
Apoorva S., Usha B. S., S. Darshan· 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
Despite the emergence of Large Language Models (LLMs), graph-based extractive summarization and specifically the TextRank algorithm have retained architectural significance in high-stakes domains including legal, medical, scientific, and journalistic text processing. This survey presents a systematic review of 121 references spanning 2002 to 2026, tracing the evolution of TextRank-based approaches into hybrid LLM pipelines and advancing three qualified arguments.First, TextRank provides architectural advantages that persist even as LLM context windows grow: source-level provenance that grounded generation methods only partially replicate; approximately 75% token consumption reduction at any fixed per-token rate; and deterministic, auditable extraction consistent with emerging regulatory transparency requirements in legal and clinical domains.Second, LLMs exhibit hallucination rates of 15–30% on domain-specific text and produce no source audit trail; TextRank pre-filtering substantially mitigates both limitations, while grounded generation alternatives address them at the generation layer. Third, hybrid TextRank→LLM pipelines achieve ROUGE-1 = 0.41 on CNN/DailyMail and 0.54–0.76 on domain-specific benchmarks; cross-benchmark ROUGE comparisons are not valid across different benchmark types and should not be interpreted as a unified performance ranking. The performance comparisons are derived from the reviewed literature; the layered pipeline architecture, limitations taxonomy, and research agenda are original contributions of this survey."
Ahmed J. Jabur, Asmaa Abdul Azeez Dakhil, Israa Saad Mohammed et al.· Iraqi Journal for Computers...· 0 citations
Unstructured documents constitute the majority of enterprise and web data. With the rapid development of large language models(LLMs), researchers have started to build data systems that analyze unstructured textual documents like operating on databases. However, because mainstream retrieval methods still relies on fuzzy matching based on vector similarity, accurately obtaining information and performing structured analysis and reasoning remains a major challenge. To address these limitations, AnnoIndex introduces two core fundamental components. The first is Annotation Index. The system uses a module called SchemaLoop to automatically create hierarchical annotation schemas from the raw corpus, and then uses lightweight language model to extract specific values. It turns scattered unstructured text into a materialized, structured index that enables low-cost filtering and querying. The annotation index avoids the black-box matching of vector similarity and amortizes attribute extraction costs from online queries to a one-time build. The second innovation is a Structured Query Engine. It compiles user questions into execution plans based on SQL extension. It first uses the Annotation Index for precise documents filtering, then gradually applies extraction operations in ascending order of cost, resorting to LLMs only for the remaining minimal fraction of the corpus that require deep semantic understanding. The extracted attributions are merged into the annotation index, reducing the cost of future queries. Experiments on three real-world datasets demonstrate that AnnoIndex consistently outperforms state-of-the-art baselines, achieving the highest average F1 score (0.87) while maintaining robust performance on complex multi-hop join and progressive reasoning queries.
The rapid growth of published research literature has made manual, unaided reading a bottleneck for students and
early-stage researchers, who must extract structured understanding from unstructured PDF documents while operating at
varying levels of comprehension. This paper presents the AI Research Partner, a full-stack MERN (MongoDB, Express.js,
React, Node.js) web platform that unifies the research-reading workflow — comprehension, synthesis, and ideation — into a
single authenticated system. The platform ingests a PDF, extracts its text, and uses the Google Gemini large language model to
generate multi-level (basic, medium, technical) section summaries, an interactive D3.js concept knowledge graph, novelty-rated
research ideas, citation recommendations, auto-generated quizzes, and abstract/slide drafts, while a Socket.io-based real-time
layer enables collaborative annotation among multiple users. The system was implemented end-to-end, evaluated through
functional testing across eight modules, and benchmarked for AI feature response latency and concurrent-user scalability.
Results indicate pass rates above 87% across all modules, typical AI response times of 3-17 seconds depending on feature
complexity, and stable real-time note-broadcast latency under load, demonstrating that a single, prompt-engineered platform can
reasonably reproduce the core stages of expert research reading within one coherent, collaborative interface.
Chintha Kameswara Lokesh, Dr. A. S. N. Chakravarthy, Priya Darshini Cholla· International Journal for Re...· 0 citations