Learning to generate summary as structured output
Hiroya Takamura, Manabu Okumura · 2010
We propose to use a structured output learning for summary generation based on the maximum coverage problem. Our method learns a function that outputs the benefit of each conceptual unit in the document cluster for this summarization model. In the training, we iteratively run a greedy algorithm that accepts items (sentences) with different costs (length) in order to generate a summary within the given maximum length limit. We empirically show that the structured output learning works well for this task and also examine its behavior in several dierent settings.