用神经网络自动总结孟加拉语新闻，提升信息获取效率
Abstractive Text Summarization for Bangla Language Using NLP and Machine Learning Approaches
- 基于神经网络构建孟加拉语摘要模型
- 实现简洁准确的文本压缩，提升处理稳定性
- 适合关注南亚语言信息处理的研究者
文本摘要旨在将长篇文档压缩为概括核心思想的简短句子。每天我们花费大量时间阅读报纸以了解国内外动态，但其中常包含与个人生活无关的冗余内容。本文提出一种针对孟加拉语的神经网络模型，可将原文自动浓缩为简明扼要的段落，旨在提高摘要的稳定性和处理效率。
原文摘要 · Abstract (English)
Text summarization involves reducing extensive documents to short sentences that encapsulate the essential ideas. The goal is to create a summary that effectively conveys the main points of the original text. We spend a significant amount of time each day reading the newspaper to stay informed about current events both domestically and internationally. While reading newspapers enriches our knowledge, we sometimes come across unnecessary content that isn't particularly relevant to our lives. In this paper, we introduce a neural network model designed to summarize Bangla text into concise and straightforward paragraphs, aiming for greater stability and efficiency.
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