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An online multi-source summarization algorithm for text readability in topic-based search

机译：基于主题的搜索中的文本可读性的在线多源摘要算法

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摘要

Web search users are likely to face problems related to the availability of large amounts of data. As the quantity of online content grows, the risk of missing relevant information during search can only increase. Moreover, external variables such as the users' reading proficiency level can further complicate the task. This article proposes an online multi-document summarization algorithm for text readability, as a means to simplify web search. The algorithm is designed to work over collections of topic-related documents, such as the ones returned as the results to a web query. Contrary to most modern approaches, no preliminary training for the algorithm is required. The algorithm was tested in both English and Spanish language documents, using different metrics of term and sentence relevance. The results were compared against summaries created by both human summarizers and third-party Automatic Text Summarization (ATS) systems in terms of two variables: readability and information content. In both variables, the results show generalized gains with respect to both the human summarizers and the third-party ATS systems. Furthermore, the algorithm achieved these results with a time complexity strictly lower than O(n~2); well below traditional machine learning approaches.
机译：Web搜索用户可能会面临与大量数据的可用性相关的问题。随着在线内容的数量增长，搜索期间缺少相关信息的风险只能增加。此外，外部变量，如用户阅读熟练程度可以进一步复杂化任务。本文提出了一种用于文本可读性的在线多文件摘要算法，作为简化Web搜索的手段。该算法旨在跨主题相关文档的集合，例如作为Web查询的结果返回的文件。与大多数现代方法相反，不需要对算法进行初步训练。该算法以英语和西班牙语文档进行测试，使用不同的术语和句子相关性的不同度量。将结果与人类摘要和第三方自动文本摘要（ATS）系统的摘要进行了比较，而是两个变量：可读性和信息内容。在两个变量中，结果显示了人类摘要和第三方ATS系统的广义增益。此外，该算法通过严格低于O（n〜2）的时间复杂度实现了这些结果;远低于传统的机器学习方法。

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