用机器学习从新闻中高效提取关键句，提升内容管理效率。
Efficient Extractive Text Summarization for Online News Articles Using Machine Learning
- 将摘要任务转为二分类，用BERT和LSTM捕捉句子顺序信息。
- 在Cornell Newsroom数据集上F1和ROUGE-1均优于基线方法。
- 适合需要自动化新闻摘要的媒体平台和内容管理系统。
在信息过载时代，在线新闻的内容管理依赖高效的摘要技术以提升可访问性和用户参与度。本文针对抽取式文本摘要问题，采用先进的机器学习方法生成简洁连贯的摘要，同时保留原文含义。基于包含130万对文章-摘要的Cornell Newsroom数据集，我们构建了利用BERT嵌入将文本转化为数值表示的流程。通过将任务建模为二分类问题，对比了逻辑回归、前馈神经网络和长短期记忆（LSTM）网络等多种模型。结果表明，具备序列依赖捕捉能力的LSTM网络在F1分数和ROUGE-1指标上均优于基线方法（如Lede-3）及简单模型。本研究凸显了自动化摘要在改善在线新闻平台内容管理系统方面的潜力，有助于实现更高效的内容组织与用户体验提升。
原文摘要 · Abstract (English)
In the age of information overload, content management for online news articles relies on efficient summarization to enhance accessibility and user engagement. This article addresses the challenge of extractive text summarization by employing advanced machine learning techniques to generate concise and coherent summaries while preserving the original meaning. Using the Cornell Newsroom dataset, comprising 1.3 million article-summary pairs, we developed a pipeline leveraging BERT embeddings to transform textual data into numerical representations. By framing the task as a binary classification problem, we explored various models, including logistic regression, feed-forward neural networks, and long short-term memory (LSTM) networks. Our findings demonstrate that LSTM networks, with their ability to capture sequential dependencies, outperform baseline methods like Lede-3 and simpler models in F1 score and ROUGE-1 metrics. This study underscores the potential of automated summarization in improving content management systems for online news platforms, enabling more efficient content organization and enhanced user experiences.
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