R. Suganya
Assistant Professor
Department of Computer Science and Engineering,
RAAK College of Engineering and Technology, Puducherry, India.
email: suganyasmvec@gmail.com.
---------------------------------------------------------------------------------
Abstract:
Especially in the era of big data, extraction and summarization of short but meaningful phrases are confronted by more and more natural language processing tools. In this work we propose the context-aware summarization methods with the enhancements both based on pre-trained models Enhanced RoBERTa with HEAD architectures by structured domain information. It is grounded on auditory attention and augments a general purpose transformer stack with knowledge-driven features to obtain more coherent, informative and semantically faithful summaries. Training and Evaluation 4.1 Corpus and Preprocessing We have corpus and preprocessing pipeline as follows. We evaluate the proposed framework using the CNN/DailyMail dataset and compare it with a variety of baselines on standard ROUGE metrics. The experimental results are quite promising for context-depth capturing and improving the quality of the automatic summaries as compared to the baselines. Overall, this work contributes to the development of text summarization by migrating from transformer-based contextual learning to integration of structured knowledge, and provides scalable and adaptable methods for intelligent information retrieval.
Keywords:
Abstractive Summarization, Enhanced RoBERTa, Information Retrieval, Natural Language Processing, ROUGE Evaluation, Transformer Models.
1. INTRODUCTION
The whole world is now creating digital information at an exponential rate and the emphasis is on tools that enable users to digest mountainous texts and actually do something with them.” Extractive summarization is one of such challenging problem in NLP where user needs to fetch the salvaged up version of input text while preserving the information of actual document. Traditional summarizers generally cannot capture the rich context, which is much harder to obtain for complex or large-scale textual contents. To overcome this limitation, various approaches have demonstrated capability in exploiting domain knowledge or embedding a state-of-the-art language model to produce summaries which are more relevant and informative.
In this paper, we present a new framework for CAS by utilizing the RoBERTa, which is an improved version of BERT with better performance in NLP tasks than BERT. As one of the models that could capture the deeper semantic hierarchy within text, RoBERTa is an excellent candidate, when we use the embeddings alongside context. To enhance the summary quality, the model exploits structured knowledge from domain corpora. This combination of both makes the summarization aware of context and domain-specific knowledge which are important for enhancing alertness and correctness of results. We test the proposed method on the very widely used CNN/DailyMail dataset, which is suitable for abstractive summarization due to its rich news articles and headlines. We test ROUGE scores to make full comparison with other baselines. This showed that RoBERTa and structured knowledge could be effectively combined to achieve superior context-aware abstraction performance.
This provides a step towards summarization through combining strong language understanding with structured domain knowledge. It also opens the door to more advanced NLP systems to handle diverse domain-specific, context-rich applications.
2. RELATED WORKS
In order to address these limitations of biomedical text summarization, we present a domain knowledge-empowered graph topic transformer, which integrates the graph-based neural topic modeling and domain-specific knowledge from the Unified Medical Language System (UMLS) with a generic transformer backbone. Such a hybrid architecture have led to more coherence and explainability than state-of-the-art PLM-driven models [1]. SE4ExSum also integrates a BERT encoder over a Feature Graph-of-Words (FGOW)-Graph Convolutional Network (GCN) model and outperforms baseline extractive summarization models showing the effectiveness of deep learning in summarization as well [2]. Another example is Event Knowledge-Guided Summarization (EKGS) paradigm applied on Weibo for meteorological events. By bringing information on event level in the summarization task, EKGS is capable to deliver informative panoramas for improved decision-taking and has been demonstrated to be applicable in practice as online service [3].
The learning knowledge graph embeddings have also evolved with larger and larger techniques, ranging from methods encoding strategies, scoring functions, graphic integration operations, and training schemas. They have been applied to different tasks such as graph completion, multilingual alignment, relation extraction, recommendation systems, among others [4]. Cross-Modal Knowledge-Guided Model (KM-KGM) KM-KGM is an enhancement model of BERT with a multimodal knowledge graph leveraging to better support the factual consistency and propose the coherency of the generated summary [5].
Additionally, hybrid systems that combine textual with KG-based models, such as those that employ personalized PageRank and GCNs, show increased robustness and accuracy [6].
In another related sentiment work, Syntax and Knowledge-Based GCN (SK-GCN) uses dependency structures and general knowledge resources to obtain good results for aspect-level classification [7]. More comprehensive surveys on the topic of knowledgeaware summarization include taxonomies of embedding methods and further examination of challenges and research directions [8]. Similarly, Sentic-GCN uses affective knowledge from SenticNet to enhance the GCN over sentiment analysis applied to benchmarks [9]. For classification, Knowledge-Based Deep Inception (KBDI) integrates BERT embeddings and KG features for webpage classification and outperforms baseline approaches [10].
In general, the traditional summarization methods which incorporate the BERT embeddings tend to employ extractive methods, such as sentence classification and ranking on CNN/DailyMail datasets [11] [12]. Meanwhile, BERT-ConvE [13] extends BERT with ConvE for KG completion, obtaining better performance in accuracy for KG completion on sparse graphs and industry data. The BKRL (BERT-CNN Knowledge Representation Learning) model combines structure-based and text-enhanced signals for semantics learning, and obtains better link prediction performance [14].
For biomedical RE, KGAGN adopts KG-guided attention with GCNs to represent chemical–disease associations, outperforming previous works on the BioCreative-V Challenge (BC-V) [15]. A similar multi-layer fusion GCN was developed for herb (leaves) recommendation using a knowledge graph of herbal properties to enhance the feature learning, which also benefit symptom–herb correlations [16]. Previous works on ATS [17] provide an overview of the evolution of the field from classical methods to deep learning models and discuss features, datasets, evaluation approaches, and future challenges. In the domain of COVID-19, Co-BERT improves unsupervised open information extraction based on entity dictionaries and achieves gains over baseline BERT systems [18]. K-BERT conducts pre-training on SMILES strings for molecule property prediction, which obtained better prediction accuracy on pharmaceutical dataset than descriptor and graph based baselines [19]. Recently, knowledge-aware fine-tuning methods built on top of hierarchical relational graph message passing have been studied to incorporate background KGs into PLMs to further improve [20].
3. PROPOSED MODEL
The process was initiated with raw input text cleaning; then, tokenization, and alignment with structured knowledge. This structured knowledge, consists of knowledge graphs or semantic relations, integrated to help with contextual grounding.
Deep contextual embeddings for the input text are constructed using the Enhanced RoBERTa module. This enhanced model is better than the RoBERTa base model in that in addition to the local masking policy, it also integrates domain knowledge of the structured corpus to allow the model to capture weak semantic relation information.
Enhanced RoBERTa context-enhanced embeddings are concatenated with domain-specific features to create a rich representation. The generated representations are then passed through a decoding unit that with the ability to generate coherently, well-structured, and abstractive summaries which can also preserve significant clauses from the input.
We also experiment on the widely used CNN/DailyMail dataset for summarization. We evaluate with standard ROUGE metrics on different aspects of summary quality – informativeness, fluency, and contextual relevance.
