An API-based search system for one click access to information

This paper proposes a prototype One Click access system, based on previous work in the field and the related 1CLICK-2@NTCIR10 task. The proposed solution integrates methods from into a three tier algorithm: query categorization, information extraction and output generation and offers suggestions on how each of these can be implemented. Finally, a thorough user-based evaluation concludes that such an information retrieval system outperforms the textual preview collected from Google search results, based on a paired sign test. Based on validation results suggestions for future improvements are proposed

Toward abstractive multi-document summarization using submodular function-based framework, sentence compression and merging

Automatic multi-document summarization is a process of generating a summary that contains the most important information from multiple documents. In this thesis, we design an automatic multi-document summarization system using different abstraction-based methods and submodularity. Our proposed model considers summarization as a budgeted submodular function maximization problem. The model integrates three important measures of a summary - namely importance, coverage, and non-redundancy, and we design a submodular function for each of them. In addition, we integrate sentence compression and sentence merging. When evaluated on the DUC 2004 data set, our generic summarizer has outperformed the state-of-the-art summarization systems in terms of ROUGE-1 recall and f1-measure. For query-focused summarization, we used the DUC 2007 data set where our system achieves statistically similar results to several well-established methods in terms of the ROUGE-2 measure