There has been great progress on automatic summarization over the past two decades, most notably approaches based on integer linear programming (ILP). In this talk I’ll present some recent work on summarization, focusing first on extractive summarization.  I will describe work with my students on the use of a supervised regression model for bigram weight estimation and the application of an ILP model to maximize the bigram coverage in the summaries.

In the second part of the talk, I will discuss new approaches to compressive summarization, moving a step closer to abstractive summarization.  Using a pipeline compression and summarization framework, I will show how to create a summary guided compression module and to use it for generating multiple compression candidates.  Compressed summary sentences are selected from these candidates by the application of a subsequent ILP system.

Finally, I will introduce a graph-cut based method for joint compression and summarization. This is more efficient than the standard ILP-based joint compressive summarization method, and has the flexibility to incorporate grammar constraints in order to generate summaries with better readability. I will present various experimental results to demonstrate the effectiveness of our approaches.