BERT-VBD: A Novel Vietnamese Multi-Document Summarization Framework Combining Extractive and Abstractive Techniques

Temel Kavramlar

The proposed framework leverages a two-component pipeline architecture that integrates extractive and abstractive summarization techniques to generate high-quality Vietnamese multi-document summaries.

Özet

The paper presents a novel Vietnamese multi-document summarization (MDS) framework that combines extractive and abstractive approaches in a pipeline architecture.

The key components are:

Data Pre-processing:

• Normalization: Cleaning, converting to lowercase, removing meaningless words and non-alphanumeric characters
• Segmentation: Sentence splitting and word splitting

Extractive Summarization:

• Uses Sentence-BERT (SBERT) to convert sentences into dense vector representations
• Calculates sentence similarity based on cosine similarity of the vectors
• Applies k-means clustering with elbow optimization to identify key sentences

Abstractive Summarization:

• Employs the VBD-LLaMA2-7B-50b model for abstractive summarization
• The encoder maps the extracted sentences into a latent feature vector
• The decoder then autoregressively generates the final summary text

The proposed framework is evaluated on the VN-MDS dataset and demonstrates superior performance compared to state-of-the-art baselines, achieving a ROUGE-2 F1-score of 39.6%. The hybrid approach effectively combines the strengths of extractive and abstractive summarization to generate coherent and informative Vietnamese summaries.

İstatistikler

The extractive summarization component achieves a ROUGE-2 F1-score of 39.6% on the VN-MDS dataset.
The proposed hybrid framework outperforms the state-of-the-art model by Thanh et al. in terms of ROUGE-1 F1-score (70.1% vs 68.63%) and ROUGE-2 F1-score (39.6% vs 34.89%).
Compared to non-hybrid baselines like MART, KL, and LSA, the hybrid model demonstrates superior performance across ROUGE-1 and ROUGE-2 metrics.

Alıntılar

"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."
"The hybrid approach effectively combines the strengths of extractive and abstractive summarization to generate coherent and informative Vietnamese summaries."

Önemli Bilgiler Şuradan Elde Edildi

BERT-VBD: Vietnamese Multi-Document Summarization Framework

by Tuan-Cuong V... : arxiv.org 09-19-2024

https://arxiv.org/pdf/2409.12134.pdf

Daha Derin Sorular

How can the proposed framework be extended to handle multi-lingual or cross-lingual summarization tasks?

The proposed Vietnamese Multi-Document Summarization (MDS) framework can be extended to handle multi-lingual or cross-lingual summarization tasks by incorporating several strategies. Firstly, the integration of multi-lingual pre-trained models, such as mBERT or XLM-R, can facilitate the understanding and processing of multiple languages within the same framework. These models are designed to handle various languages simultaneously, allowing for effective sentence embedding and semantic understanding across different linguistic contexts.
Secondly, the framework can be adapted to include a translation component that translates documents from the source language to a target language before summarization. This could involve using advanced neural machine translation (NMT) systems to ensure that the nuances and context of the original text are preserved during translation. After summarization in the target language, a reverse translation step could be implemented to convert the summary back to the original language, if necessary.
Additionally, leveraging a hybrid approach that combines extractive and abstractive techniques across languages can enhance the summarization quality. For instance, the extractive component could identify key sentences in the source language, while the abstractive component could generate summaries in the target language, ensuring coherence and fluency. This multi-lingual framework would not only improve the accessibility of information across language barriers but also enhance the overall performance of the summarization task.

What are the potential limitations of the current hybrid approach, and how could it be further improved to handle more complex Vietnamese language constructs?

The current hybrid approach, while effective, has several potential limitations when dealing with the complexities of the Vietnamese language. One significant challenge is the language's rich morphology and syntax, which can lead to difficulties in accurately identifying salient sentences during the extractive phase. The reliance on pre-trained models like SBERT may not fully capture the unique linguistic features of Vietnamese, such as tone and context-dependent meanings, which could result in suboptimal sentence selection.
To improve the framework's handling of complex Vietnamese constructs, several enhancements can be made. Firstly, incorporating a more sophisticated segmentation and tokenization process that accounts for Vietnamese linguistic characteristics could enhance the accuracy of sentence extraction. This might involve using language-specific tokenizers that better understand the nuances of Vietnamese grammar and syntax.
Secondly, the integration of additional contextual embeddings that are specifically trained on Vietnamese corpora could improve the semantic understanding of the text. Models like PhoBERT, which are tailored for Vietnamese, could be utilized alongside SBERT to provide richer embeddings that capture the intricacies of the language.
Lastly, implementing a feedback mechanism that allows the model to learn from its summarization outputs could help refine the extractive and abstractive processes over time. By analyzing the coherence and relevance of generated summaries, the model could adapt and improve its performance on complex language constructs, ultimately leading to higher-quality summaries.

Given the advancements in large language models, how could the integration of additional pre-trained models beyond SBERT and VBD-LLaMA2-7B-50b enhance the summarization capabilities of the framework?

The integration of additional pre-trained models beyond SBERT and VBD-LLaMA2-7B-50b could significantly enhance the summarization capabilities of the proposed framework in several ways. Firstly, utilizing models like T5 (Text-to-Text Transfer Transformer) or BART (Bidirectional and Auto-Regressive Transformers) could improve the abstractive summarization component. These models are designed to generate coherent and contextually relevant text, making them well-suited for producing high-quality summaries that maintain the essence of the original documents.
Moreover, incorporating models that specialize in specific tasks, such as GPT-3 or its successors, could provide advanced language generation capabilities. These models excel in generating human-like text and could enhance the framework's ability to produce fluent and engaging summaries, particularly in the abstractive phase.
Additionally, leveraging multi-task learning frameworks that allow for simultaneous training on various summarization tasks could improve the model's robustness. By training on diverse datasets, the model could learn to generalize better across different types of documents and languages, leading to improved performance in summarization tasks.
Finally, the use of ensemble methods that combine the outputs of multiple models could further enhance summarization quality. By aggregating the strengths of different pre-trained models, the framework could produce more accurate and comprehensive summaries, effectively capturing the nuances of the source documents while ensuring readability and coherence in the final output. This multi-faceted approach would not only improve the summarization capabilities of the framework but also position it as a more versatile tool for handling diverse summarization challenges.