Natural Language Processing for Automatic Text Summarization

Nevidu Jayatilleke, Ruvan Weerasinghe, Nipuna Senanayake

The substantial growth of textual content in diverse domains and platforms has led to a considerable need for Automatic Text Summarization (ATS) techniques that aid in the process of text analysis. The effectiveness of text summarization models has been significantly enhanced in a variety of technical domains because of advancements in Natural Language Processing (NLP) and Deep Learning (DL). Despite this, the process of summarizing textual information continues to be significantly constrained by the intricate writing styles of a variety of texts, which involve a range of technical complexities. Text summarization techniques can be broadly categorized into two main types: abstractive summarization and extractive summarization. Extractive summarization involves directly extracting sentences, phrases, or segments of text from the content without making any changes. On the other hand, abstractive summarization is achieved by reconstructing the sentences, phrases, or segments from the original text using linguistic analysis. Through this study, a linguistically diverse categorizations of text summarization approaches have been addressed in a constructive manner. In this paper, the authors explored existing hybrid techniques that have employed both extractive and abstractive methodologies. In addition, the pros and cons of various approaches discussed in the literature are also investigated. Furthermore, the authors conducted a comparative analysis on different techniques and matrices to evaluate the generated summaries using language generation models. This survey endeavors to provide a comprehensive overview of ATS by presenting the progression of language processing regarding this task through a breakdown of diverse systems and architectures accompanied by technical and mathematical explanations of their operations.

Submission history

From: Nevidu Jayatilleke Mr. [view email]
[v1] Thu, 27 Feb 2025 05:17:36 UTC (2,664 KB)

Here is a detailed evaluation of the provided abstract/survey overview on Automatic Text Summarization (ATS):

Strengths:

1. Contextual Motivation:
   The abstract clearly introduces the growing volume of textual data and the consequent need for effective Automatic Text Summarization techniques. This situates the importance of the study in a real-world context.
2. Coverage of Key Concepts:
   It succinctly differentiates the two primary summarization paradigms—extractive and abstractive—providing clear definitions that help readers understand the fundamental approaches in the field.
3. Scope and Depth:
   The mention of hybrid techniques, which combine extractive and abstractive methods, signals that the survey addresses contemporary, nuanced approaches beyond the classical division.
4. Critical Analysis:
   The abstract notes that the authors investigate pros and cons of existing approaches and provide comparative analysis based on evaluation metrics, suggesting a thorough, balanced treatment of the literature.
5. Technical Insight:
   The promise of including technical and mathematical explanations alongside system and architecture descriptions indicates the survey goes beyond superficial coverage, providing a deeper understanding suitable for a technical audience.

Areas for Improvement:

1. Language and Style:
   • The writing is somewhat verbose and repetitive in places; for example, “considerable need” and “significantly constrained” could be streamlined for clarity and conciseness.
   • Phrases like “linguistically diverse categorizations…have been addressed in a constructive manner” are vague and could be more explicitly described to enhance clarity.
   • Some expressions are awkward, e.g., “matrices to evaluate” seems like a misuse of “matrices” (likely meant “metrics”).
2. Precision and Specificity:
   • The phrase “technical complexities” related to writing styles is broad and undeveloped. It would help to specify some examples (e.g., domain-specific jargon, syntax complexity).
   • The claim of “enhanced effectiveness” due to NLP and DL advancements would be stronger if supported with more concrete recent references or quantifications.
3. Structure and Flow:
   The concluding sentences attempt to summarize the scope of the paper but feel somewhat dense and could be clarified and divided for readability, such as separating the discussion of systems/architectures from the explanation of methods.
4. Consistency:
   The text shifts between referring to “this study,” “this paper,” and “the authors,” which could be unified for smoother tone.

Overall Impression:

The abstract outlines a comprehensive and technically-oriented survey of ATS that covers fundamental concepts, hybrid approaches, evaluation methods, and mathematical underpinnings. It successfully establishes the significance of the topic and the survey’s depth. However, it would benefit from clearer, more concise language, better flow, and a few more precise details to maximize accessibility and impact for readers.

View on arXiv

Related posts:

Extractive summarization Explained