Summarizing text documents: sentence selection and evaluation metrics
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A Maximum-Entropy-Inspired Parser
A Maximum-Entropy-Inspired Parser
Text classification using string kernels
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Automatic evaluation of summaries using N-gram co-occurrence statistics
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
An empirical study of information synthesis tasks
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Dependency-based sentence alignment for multiple document summarization
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Using random walks for question-focused sentence retrieval
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Learning to recognize features of valid textual entailments
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
A generalization of Haussler's convolution kernel: mapping kernel
Proceedings of the 25th international conference on Machine learning
ICTAI '08 Proceedings of the 2008 20th IEEE International Conference on Tools with Artificial Intelligence - Volume 02
Improving the performance of the random walk model for answering complex questions
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
UofL: word sense disambiguation using lexical cohesion
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
LexRank: graph-based lexical centrality as salience in text summarization
Journal of Artificial Intelligence Research
Using semantic information to answer complex questions
Canadian AI'11 Proceedings of the 24th Canadian conference on Advances in artificial intelligence
Degree centrality for semantic abstraction summarization of therapeutic studies
Journal of Biomedical Informatics
GenDocSum+MCLR: Generic document summarization based on maximum coverage and less redundancy
Expert Systems with Applications: An International Journal
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The task of answering complex questions requires inferencing and synthesizing information from multiple documents that can be seen as a kind of topic-oriented, informative multi-document summarization. In generic summarization the stochastic, graph-based random walk method to compute the relative importance of textual units (i.e. sentences) is proved to be very successful. However, the major limitation of the TF^*IDF approach is that it only retains the frequency of the words and does not take into account the sequence, syntactic and semantic information. This paper presents the impact of syntactic and semantic information in the graph-based random walk method for answering complex questions. Initially, we apply tree kernel functions to perform the similarity measures between sentences in the random walk framework. Then, we extend our work further to incorporate the Extended String Subsequence Kernel (ESSK) to perform the task in a similar manner. Experimental results show the effectiveness of the use of kernels to include the syntactic and semantic information for this task.