Self-Organizing Maps-based Graph Convolutional Summarizer -based Multi-Model Approaches for Document Summarization
Keywords:
Deep learning, Document summarization, Automatic document summarizing, Self-Organizing Maps-based Graph Convolutional Summarizer (SOM-GCS), Multi-document summarizingAbstract
A brief overview of the same subject summarizes a lengthy text or paper. Most of the paper's crucial material must be retained while superfluous verbosity is eliminated. In instruction to produce a succinct summary for a document summarizing, the system collects keywords from papers or multiple documents. The basic idea is to limit or cut back on the quantity of crucial information in any given text. An information processing system that, given a collection of documents, extracts the essential information from the source while keeping the user or task in mind, then presents the summary in well-formed and concise prose and performs the assignment of a document summarizing. Summarizing numerous documents as opposed to only one is called multi-document summarization. The two main kinds are extractive and abstractive summaries of several materials. The most important and notable phrases and words from the original text are used to construct extractive resumes. However, some terms and sentences may not be found in the original text. This article focuses on ATS (Automatic document summarizing) methods that have recently been introduced. Deep learning-based models have recently been used for multi-document summarizing, which encourages the growth of text summarization and enhances model performance. We suggest the Self-Organizing Maps-based Graph Convolutional Summarizer (SOM-GCS). This extractive multi-document summary method uses SOM to ensure minimum performance constraints as an alternative to standard approaches. It fixes SMO-GCS's flaws and adds improvements that lead to a summarizer that enables phrase embedding and feature learning that is conscious of the graph structure. A rigorous methodology is needed to demonstrate how improvements are possible while still guaranteeing a minimal performance restriction. The effectiveness of the suggested summarizing approach is assessed using the DUC 2004 and Daily Mail/CNN datasets. The experimental findings show that SOM-GCS performs comparably to state-of-the-art summarization methods regarding ROUGE scores.
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