Textual Summarization of Scientific Publications and Usage Patterns
Aybüke Öztürk · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2012
In this study, we propose textual summarization for scientific publications and mobile phone usage patterns. Textual summarization is a process that takes a source document or set of related documents, identifying the most salient information and conveying it in less space than the original text. The increasing availability of information has necessitated deep research for textual summarization within Information Retrieval and the Natural Language Processing (NLP) area because textual summaries are easier to read, and provide to access to large repositories of content data in an efficient way. For example, snippets in web search are helpful for users as textual summaries. While there exists summarization tools for textual summarization, either they are not adapted to scientific collection of documents or they summarize short form of text such as news. In the first part of this study, we adapt the MEAD 3.11 summarization tool [19] to propose a method for building summaries of a set of related scientific articles by exploiting the structure of scientific publications in order to focus on some parts that are known to be the most informative in such documents. In the second part, we generate a natural language statement that describes a more readable form of a given symbolic pattern extracted from Nokia Challenge data. The reason is that the availability of mobile phone usage details enables new opportunities to provide a better understanding of the interest of user populations in mobile phone applications. For evaluating the first part of study, we make use of Amazon Mechanical Turk (Mturk) to validate summarization output.