Extractive Multi-Document Summarization Using Transformer-Based Neural Networks

The management and extraction of pertinent information from sizable text collections depends critically on multi-document summarization. Using Transformer-based neural networks, this research describes a technical method for extractive multi-document summarization. To capture the important information across many publications, we suggest a unique architecture that makes use of self-attention processes and contextualized word representations. To improve the summary generation process, the model is trained using a combination of supervised learning and reinforcement learning methods. We run thorough tests on benchmark datasets and evaluate our method against current state-of-the-art techniques. The outcomes show how well our suggested model performs in producing informative and cogent summaries from a variety of documents.