A Generative Approach for Multi-Document Summarization using the Noisy Channel Model

Multi-document summarization is the automatic prod uction of a unique summary from a collection of texts. This task has become ve ry important, since it assists the information processing in days where the amount of information is growing considerably. In this paper, we propose a statistical generative approach for multi -document summarization. In particular, we formulate the multi-document summarization task usi ng a Noisy-Channel model. This approach is novel for multi-document summarization and it ex plores the process of summarization through the analysis of factors, such as redundancy , complementarity and contradiction. In this work, we model these factors using the Cross-docume nt Structure Theory.