Incremental Text Structuring with Online Hierarchical Ranking

Many emerging applications require documents to be repeatedly updated. Such documents include newsfeeds, webpages, and shared community resources such as Wikipedia. In this paper we address the task of inserting new information into existing texts. In particular, we wish to determine the best location in a text for a given piece of new information. For this process to succeed, the insertion algorithm should be informed by the existing document structure. Lengthy real-world texts are often hierarchically organized into chapters, sections, and paragraphs. We present an online ranking model which exploits this hierarchical structure – representationally in its features and algorithmically in its learning procedure. When tested on a corpus of Wikipedia articles, our hierarchically informed model predicts the correct insertion paragraph more accurately than baseline methods.

• Type: Conference
• Year: 2007
• Where: EMNLP
• Authors: Erdong Chen, Benjamin Snyder, Regina Barzilay
• Keywords: Correct Insertion Paragraph, EMNLP 2007, Lengthy Real-world Texts, Many Emerging Applications, Natural Language Processing