Extract with Order for Coherent Multi-Document Summarization
This work addresses the challenge of generating coherent and informative summaries from multiple documents, which is incremental as it builds on existing methods with specific enhancements.
The paper tackled the problem of extractive multi-document summarization by developing a rank-based sentence selection method using continuous vector representations and key-phrases, along with a model to improve summary coherence, resulting in significant improvements over state-of-the-art methods in informativity and coherence on the DUC 2004 datasets.
In this work, we aim at developing an extractive summarizer in the multi-document setting. We implement a rank based sentence selection using continuous vector representations along with key-phrases. Furthermore, we propose a model to tackle summary coherence for increasing readability. We conduct experiments on the Document Understanding Conference (DUC) 2004 datasets using ROUGE toolkit. Our experiments demonstrate that the methods bring significant improvements over the state of the art methods in terms of informativity and coherence.