Advancements in Natural Language Processing for Automatic Text Summarization

2024-09-20

International Conference on Conceptual Structures

AI Summary

To address performance bottlenecks in automatic text summarization (ATS) arising from cross-domain, multi-platform textual proliferation—exacerbated by linguistic stylistic diversity and technical complexity—this paper presents a systematic survey and methodological innovations. We propose the first taxonomy of ATS methods explicitly designed for linguistic style variability; develop a hybrid evaluation matrix integrating linguistic features with deep learning to unify assessment criteria across extractive, abstractive, and hybrid paradigms; and empirically delineate the performance boundaries and applicability scopes of mainstream models via BLEU/ROUGE metrics and controlled comparative experiments. The work delivers a reproducible evaluation benchmark, a principled framework for model selection, and a roadmap for architectural evolution—thereby substantially enhancing ATS generalizability and interpretability over heterogeneous texts.

Technology Category

• Natural Language Processing: Summarization
• Machine Learning: Large Multimodal Models (LMMs)
• Application Domains: Humanities & Computational Social Science

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

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.

Problem

• Enhance Automatic Text Summarization techniques
• Address linguistic diversity in summarization
• Compare hybrid extractive and abstractive methods

Innovation

• Hybrid extractive and abstractive summarization
• Comparative analysis of summarization techniques
• Linguistic analysis for text reconstruction