A Computationally Efficient System for High-Performance Multi-Document Summarization
We propose and develop a simple and efficient algorithm for generating extractive multi-document summaries and show that this algorithm exhibits state-of-the-art or near state-of-the-art performance on two Document Understanding Conference datasets and two Text Analysis Conference datasets. Our results show that algorithms using simple features and computationally efficient methods are competitive with much more complex methods for multi-document summarization (MDS). Given these findings, we believe that our summarization algorithm can be used as a baseline in future MDS evaluations. Further, evidence shows that our system is near the upper limit of performance for extractive MDS.
Sovine, S., & Han, H. (2013, May). A Computationally Efficient System for High-Performance Multi-Document Summarization. In The Twenty-Sixth International Florida Artificial Intelligence Research Society Conference. http://aaai.org/ocs/index.php/FLAIRS/FLAIRS13/paper/view/5940