Automation of summarization evaluation methods and their application to the summarization process
Summarization is the process of creating a more compact textual representation of a document or a collection of documents. In view of the vast increase in electronically available information sources in the last decade, filters such as automatically generated summaries are becoming ever more important to facilitate the efficient acquisition and use of required information. Different methods using natural language processing (NLP) techniques are being used to this end. One of the shallowest approaches is the clustering of available documents and the representation of the resulting clusters by one of the documents; an example of this approach is the Google News website. It is also possible to augment the clustering of documents with a summarization process, which would result in a more balanced representation of the information in the cluster, NewsBlaster being an example. However, while some systems are already available on the web, summarization is still considered a difficult problem in the NLP community. One of the major problems hampering the development of proficient summarization systems is the evaluation of the (true) quality of system-generated summaries. This is exemplified by the fact that the current state-of-the-art evaluation method to assess the information content of summaries, the Pyramid evaluation scheme, is a manual procedure. In this light, this thesis has three main objectives. 1. The development of a fully automated evaluation method. The proposed scheme is rooted in the ideas underlying the Pyramid evaluation scheme and makes use of deep syntactic information and lexical semantics. Its performance improves notably on previous automated evaluation methods. 2. The development of an automatic summarization system which draws on the conceptual idea of the Pyramid evaluation scheme and the techniques developed for the proposed evaluation system. The approach features the algorithm for determining the pyramid and bases importance on the number of occurrences of the variable-sized contributors of the pyramid as opposed to word-based methods exploited elsewhere. 3. The development of a text coherence component that can be used for obtaining the best ordering of the sentences in a summary.