Enhancing Technical Documentation through Intelligent Text Summarization Techniques

Abstract

In an era of fast digital transformation, technical documentation is more important than ever in aiding user knowledge, upkeep of systems, and operational efficiency across a variety of organizations. However, the ever-growing complexity of software platforms, enterprise applications, and IT infrastructures has resulted in a massive amount of technical content that is challenging to navigate and time-consuming to comprehend. Users, including developers, executives, end users, and support engineers, deserve accurate and easily accessible documentation. This study investigates the use of text summarizing techniques in technical documentation workflows to address the issues and improve the overall quality, usability, and efficacy of such content. Text Summarization (TS) entails condensing extensive text into brief forms while preserving its basic meaning. In technical documentation, this feature promotes faster information extraction, comprehension, and user engagement. The study defines two main summary techniques—extractive and abstractive—and assesses their efficacy in a documentation setting. Extractive summarization extracts essential lines or phrases straight from the source material while keeping the underlying structure and vocabulary, which is especially useful in circumstances that need technical precision. In contrast, abstractive summarization paraphrases and rewrites the text in a more reduced manner, resulting in greater fluidity and readability. This study proposes a hybrid model that combines these approaches to achieve a balance of clarity and accuracy. The process involves integrating traditional and transformer-based models like BERT, T5, and PEGASUS to technical documentation datasets. Using supervised fine-tuning and domain-specific corpora, the models are trained to provide summaries that are suited to different user needs. Finally, using text summarizing algorithms in technical documentation is a significant step toward more efficient, user-friendly, and intelligent content delivery. This study establishes the groundwork for creating adaptable documentation systems that match the changing needs of current users.

Introduction

1. Complexity of Enterprise IT Systems and Documentation Challenges

Enterprise IT systems are inherently complex due to their vast size, integration of multiple technologies, varied user base, and critical business operations. These systems often feature legacy components and constant updates, making them difficult to manage without clear and user-friendly documentation. Traditional documentation often fails users by being overly technical, jargon-heavy, and structured linearly, prioritizing technical accuracy over usability. This results in user frustration, increased support costs, and inefficiencies.

2. Importance of Human-Centered Design (HCD) in Documentation

To enhance documentation usability, HCD principles should be adopted. This involves understanding user needs through interviews, usability tests, and behavior analysis. Documentation should be tailored to user personas (novices, admins, support staff) and feature clear language, visual aids, modular structure, and accessible design (e.g., WCAG compliance). Integrating real-time help (tooltips, chatbots) and analytics can further improve clarity and relevance while reducing cognitive load.

3. Handling Documentation Glitches

To maintain quality, common issues such as broken links, inconsistencies, or ambiguous instructions must be addressed via:

• User-centered testing (usability tests, feedback mechanisms)

• Consistency standards (style guides, templates)

• Automation tools (grammar checkers, link/code validators)

• Version control and structured authoring (topic-based writing, XML/Markdown)

• Peer reviews and readability tools (e.g., Hemingway, WCAG tools)

4. Role and Impact of Text Summarization (TS)

TS helps distill complex technical content into concise, accessible summaries. It:

• Improves readability and comprehension

• Reduces support burden and user frustration

• Supports layered content delivery (brief overviews + deep dives)

• Enhances searchability and AI-tool effectiveness (e.g., chatbots, auto-suggestions)

TS aligns with HCD by catering to users with different technical levels and time constraints. It’s especially useful in manuals, API docs, release notes, and UI-integrated help systems.

5. Literature Review

The literature survey covers advancements in TS, especially those powered by Deep Learning (DL) and Natural Language Processing (NLP). Key findings include:

• Pre-trained models like BERT, T5, and PEGASUS offer strong summarization capabilities.

• Various DL approaches (Seq2Seq, RL, TL) enhance performance across tasks.

• Studies applied summarization to multiple languages, social media, and legal texts, showing high accuracy and usability.

• Extractive and abstractive summarization methods both show promise, though extractive is easier to implement.

6. Generalized Methodology

A structured research methodology ensures the development of effective, usable documentation. This includes:

• User research

• Content modeling

• HCD-based design iterations

• Feedback integration

• Automation and analytics for continuous improvement

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Copyright

Copyright © 2025 Rajpal Kaur. 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.