Automatic summarization can help users extract the most important pieces of information from the vast amount of text digitized into electronic form everyday. Central to automatic summarization is the notion of similarity between sentences in text. In this paper we propose the use of continuous vector representations for semantically aware representations of sentences as a basis for measuring similarity. We evaluate different compositions for sentence representation on a standard dataset using the ROUGE evaluation measures. Our experiments show that the evaluated methods improve the performance of a state-of-the-art summarization framework and strongly indicate the benefits of continuous word vector representations for automatic summarization.
In REF , the authors proposed the use of vector representations of sentences as a metric of similarity in the process of extractive summarization.
Extractive Summarization using Continuous Vector Space Models
Proceedings of the 2nd Workshop on Continuous Vector Space Models and their Compositionality (CVSC)
Proceedings of the 2nd Workshop on Continuous Vector Space Models and their Compositionality (CVSC)
Computer Science