Semantic-Aware Text Extraction and Neural Topic Discovery Using Pretrained Language Models

Volume no :

6 |

Issue no :

1

Article Type :

Scholarly Article

Author :

Arul Selvan M

Published Date :

May, 2025

Publisher :

Journal of Science Technology and Research (JSTAR)

Page No: 1 - 12

Abstract : This paper presents a novel framework that leverages pretrained language models to enhance semantic-aware text extraction and neural topic discovery from large and unstructured text corpora. Traditional topic modeling techniques, such as Latent Dirichlet Allocation (LDA), often rely on bag-of-words representations, which ignore the semantic relationships between words and suffer from limited contextual understanding. To address these limitations, our approach integrates contextual embeddings derived from state-of-the-art pretrained language models, such as BERT and its variants, enabling a richer and more nuanced representation of text semantics. Our framework begins by extracting semantically meaningful text segments using attention-based mechanisms inherent to pretrained models, allowing us to capture relevant context beyond simple keyword frequency. This step significantly improves the quality of text extraction by focusing on conceptually important phrases and sentences rather than isolated terms. Subsequently, we utilize neural topic discovery techniques that operate directly on the dense embeddings produced by the language models, bypassing the traditional reliance on sparse count-based features. We introduce a novel neural architecture that combines contrastive learning with clustering objectives to discover coherent and interpretable topics from these semantic embeddings. This architecture dynamically adapts to the semantic structures within the data, improving both the granularity and coherence of discovered topics. Experimental results on multiple benchmark datasets demonstrate that our method outperforms conventional topic models in terms of topic coherence and relevance, as measured by standard evaluation metrics such as topic coherence scores (e.g., NPMI, UMass) and human qualitative assessments. Additionally, our approach exhibits robustness across various domains and scales efficiently to large datasets, making it applicable for real-world scenarios such as document summarization, content recommendation, and exploratory data analysis. We also provide comprehensive ablation studies illustrating the contributions of semantic-aware extraction and neural topic modeling components to overall system performance. In conclusion, our work highlights the potential of pretrained language models not only for improving semantic representation but also as foundational tools for downstream tasks involving text extraction and topic discovery. By bridging the gap between deep contextual understanding and neural topic modeling, this framework offers a powerful paradigm for interpreting and organizing complex textual information.

Keyword: pretrained language models, semantic-aware text extraction, neural topic discovery, BERT, contextual embeddings, topic modeling, contrastive learning, clustering, topic coherence, natural language processing, document summarization, text representation, unsupervised learning, deep learning, semantic understanding

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Semantic-Aware Text Extraction

Semantic-Aware Text Extraction with the exponential growth of digital text data generated daily—from social media posts and
news articles to academic papers and corporate documents—there is an increasing need for
automated methods that can effectively extract meaningful information and uncover latent
topics within large unstructured text corpora. Understanding the thematic structure of
documents not only aids in organizing and summarizing content but also supports numerous
downstream applications such as information retrieval, recommendation systems, and
knowledge discovery. Traditional topic modeling approaches, such as Latent Dirichlet Allocation
(LDA) and Non-negative Matrix Factorization (NMF), have played a pivotal role in this domain by
identifying groups of co-occurring words that represent underlying topics. However, these
models often rely on bag-of-words representations that disregard the semantic context of terms,
resulting in limited interpretability and performance, especially on complex or nuanced datasets.
In recent years, the advent of pretrained language models (PLMs), such as BERT (Bidirectional
Encoder Representations from Transformers), GPT (Generative Pretrained Transformer), and
their derivatives, has revolutionized natural language processing (NLP). These models are trained
on massive corpora using self-supervised objectives, enabling them to capture deep contextual
relationships between words in a sentence or document. Unlike traditional methods that treat
words as discrete tokens, PLMs generate dense, context-aware embeddings that encapsulate
semantic meaning and syntactic nuances. Leveraging these embeddings for downstream tasks
has led to remarkable improvements in performance across a wide spectrum of NLP challenges,
including sentiment analysis, question answering, and text classification.
Despite the success of PLMs, their potential for enhancing topic discovery remains relatively
underexplored. Most classical topic modeling techniques cannot directly incorporate denseWith the exponential growth of digital text data generated daily—from social media posts and
news articles to academic papers and corporate documents—there is an increasing need for
automated methods that can effectively extract meaningful information and uncover latent
topics within large unstructured text corpora. Understanding the thematic structure of
documents not only aids in organizing and summarizing content but also supports numerous
downstream applications such as information retrieval, recommendation systems, and
knowledge discovery. Traditional topic modeling approaches, such as Latent Dirichlet Allocation
(LDA) and Non-negative Matrix Factorization (NMF), have played a pivotal role in this domain by
identifying groups of co-occurring words that represent underlying topics. However, these
models often rely on bag-of-words representations that disregard the semantic context of terms,
resulting in limited interpretability and performance, especially on complex or nuanced datasets.
In recent years, the advent of pretrained language models (PLMs), such as BERT (Bidirectional
Encoder Representations from Transformers), GPT (Generative Pretrained Transformer), and
their derivatives, has revolutionized natural language processing (NLP). These models are trained
on massive corpora using self-supervised objectives, enabling them to capture deep contextual
relationships between words in a sentence or document. Unlike traditional methods that treat
words as discrete tokens, PLMs generate dense, context-aware embeddings that encapsulate
semantic meaning and syntactic nuances. Leveraging these embeddings for downstream tasks
has led to remarkable improvements in performance across a wide spectrum of NLP challenges,
including sentiment analysis, question answering, and text classification.
Despite the success of PLMs, their potential for enhancing topic discovery remains relatively
underexplored. Most classical topic modeling techniques cannot directly incorporate dense