| A risk minimization framework for extractive summarization | sentences of a given document can be iteratively chosen (i.e., one at each iteration) from the document until the aggregated summary reaches a predefined target summarization ratio. |
| Proposed Methods | Once the sentence generative model P(13 | S j), the sentence prior model P(Sj) and the loss function L(Si,Sj) have been properly estimated, the summary sentences can be selected iteratively by (8) according to a predefined target summarization ratio. |
| Proposed Methods | To alleviate this problem, the concept of maximum marginal relevance (MMR) (Carbonell and Goldstein, 1998), which performs sentence selection iteratively by striking the balance between topic relevance and coverage, can be incorporated into the loss function: |