Automatic Text Summarization of Frequently Asked Questions Web Pages

This research presented in this book is directed towards automating frequently asked questions Web pages summarization, a task that captures the most salient pieces of information in answers of each question. In fact, to achieve this objective, an approach, which applies Web page segmentation to detect Q/A ¿Question and Answer-units along with the use of some selective statistical sentence extraction features for summary generation, is proposed. In conclusion, it was found that our approach performs much better than its competitive commercial summarization tool. In fact, the overall average scores for all pages examined indicate that it is superior to the other system by approximately 20% which seems to be quite promising. In addition, the overall average for all pages indicates statistical significant improvements for our approach in 62% of the cases. Moreover, considering human evaluators, it was found out that the average improvement for all evaluators is in favor of our new approach by approximately 26.3 %.