BERT-VBD: Vietnamese Multi-Document Summarization Framework (2409.12134v1)

Abstract: In tackling the challenge of Multi-Document Summarization (MDS), numerous methods have been proposed, spanning both extractive and abstractive summarization techniques. However, each approach has its own limitations, making it less effective to rely solely on either one. An emerging and promising strategy involves a synergistic fusion of extractive and abstractive summarization methods. Despite the plethora of studies in this domain, research on the combined methodology remains scarce, particularly in the context of Vietnamese language processing. This paper presents a novel Vietnamese MDS framework leveraging a two-component pipeline architecture that integrates extractive and abstractive techniques. The first component employs an extractive approach to identify key sentences within each document. This is achieved by a modification of the pre-trained BERT network, which derives semantically meaningful phrase embeddings using siamese and triplet network structures. The second component utilizes the VBD-LLaMA2-7B-50b model for abstractive summarization, ultimately generating the final summary document. Our proposed framework demonstrates a positive performance, attaining ROUGE-2 scores of 39.6% on the VN-MDS dataset and outperforming the state-of-the-art baselines.

An Overview of BERT-VBD: Vietnamese Multi-Document Summarization Framework

The paper of multi-document summarization (MDS), particularly within the intricate and less explored Vietnamese language domain, offers unique challenges demanding innovative approaches. The work titled "BERT-VBD: Vietnamese Multi-Document Summarization Framework" seeks to provide an advanced solution to these challenges through the integration of both extractive and abstractive summarization techniques. This paper posits a novel framework implementing a two-component pipeline architecture to address the limitations of singular summarization methodologies by combining the strengths of both extractive and abstractive approaches.

The framework utilizes an extractive component based on Sentence-BERT (SBERT) to discern pivotal sentences within a document, prioritizing semantically significant embeddings through advanced neural network structures such as siamese and triplet networks. This front-end processing serves as the initial step in the pipeline, which identifies and clusters sentences pivotal for summarization. These clusters constitute the input to a subsequent component that employs the VBD-LLaMA2-7B-50b model, a sophisticated model derived for abstractive summarization, culminating in the creation of concise, digestible summaries from larger document corpuses.

The empirical results on the VN-MDS dataset manifest the utility of this framework. The model achieves an impressive ROUGE-2 F1-score of 39.6%, providing evidence of its efficacy as it surpasses the benchmarks offered by previous state-of-the-art models. Such performance metrics underscore the model's capability to retain vital content and generate coherent, succinct summaries, which are essential for applications requiring the synthesis of information from multiple sources.

The implications of this research are twofold: firstly, it addresses the inherent complexity involved in Vietnamese language processing, providing a robust framework adaptable to similar linguistic challenges. Secondly, it establishes a foundational model that can be expanded or adapted for other domains or languages that would benefit from hybrid summarization approaches.

Theoretically, this work promotes the viability of hybrid models for NLP tasks, particularly within the context of summarization, where preserving meaning while ensuring readability is crucial. The implementation of advanced models like SBERT and VBD-LLaMA2-7B-50b within a synergistic framework suggests a forward path for enhancing contextual understanding and the generation of concise outputs. It highlights the potential to integrate robust pre-trained models with skilled engineering of the overall processing pipeline, allowing researchers to optimize computational resources effectively.

Future work could extend this framework to other languages or dialects, evaluating the adaptability of hybrid approaches in different linguistic constructs or expanding the model's capabilities in handling unstructured data. Furthermore, as the landscape of NLP continues to evolve with increasingly sophisticated models, exploring further enhancements or alternative methodologies aligned with cutting-edge developments could significantly enrich the outcomes of MDS tasks.

In summation, this work provides a substantial contribution to the field of multi-document summarization, particularly within the Vietnamese language space, elucidating the benefits of a hybrid model framework. It offers compelling evidence that integrating pre-trained models within a strategic pipeline can yield substantial improvements in the quality and efficiency of document summarization, setting a promising course for future advancements in this area.