使用神经序列标记模型去除样板
Boilerplate Removal using a Neural Sequence Labeling Model
摘要：
从网页中提取主要内容对于许多应用来说是一项重要的任务，这些应用的范围从可用性方面，如读者在web浏览器中查看新闻文章，到信息检索或自然语言处理。现有的方法是缺乏的，因为它们依赖于大量的手工制作的分类功能。这导致模型被定制为特定的网页分布，例如从某个时间范围，但缺乏泛化能力。我们提出了一个神经序列标签模型，它不依赖于任何手工制作的功能，但只需要HTML标签和出现在网页中的单词作为输入。这使我们能够提供一个浏览器扩展，它使用我们的模型直接在浏览器中突出显示任意网页的内容。此外，我们创建了一个新的，更当前的数据集，以表明我们的模型能够适应网页结构的变化，并优于最先进的模型。
英文摘要：
The extraction of main content from web pages is an important task for numerous applications, ranging from usability aspects, like reader views for news articles in web browsers, to information retrieval or natural language processing. Existing approaches are lacking as they rely on large amounts of hand-crafted features for classification. This results in models that are tailored to a specific distribution of web pages, e.g. from a certain time frame, but lack in generalization power. We propose a neural sequence labeling model that does not rely on any hand-crafted features but takes only the HTML tags and words that appear in a web page as input. This allows us to present a browser extension which highlights the content of arbitrary web pages directly within the browser using our model. In addition, we create a new, more current dataset to show that our model is able to adapt to changes in the structure of web pages and outperform the state-of-the-art model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
