We present a new method to generate extractive multi-document summaries. The method uses Integer Linear Programming to jointly maximize the importance of the sentences it includes in the summary and their diversity, without exceeding a maximum allowed summary length. To obtain an importance score for each sentence, it uses a Support Vector Regression model trained on human-authored summaries, whereas the diversity of the selected sentences is measured as the number of distinct word bigrams in the resulting summary. Experimental results on widely used benchmarks show that our method achieves state of the art results, when compared to competitive extractive summarizers, while being computationally efficient as well.
Support vector regression (SVR) has also been used in conjunction with other techniques like integer linear programming (ILP) for generating summaries REF and has achieved state-of-the-art results in comparison to other competitive extractive summarisers.
Extractive Multi-Document Summarization with Integer Linear Programming and Support Vector Regression
COLING
Computer Science