  Since the amount of information on the internet is growing rapidly, it is not
easy for a user to find relevant information for his/her query. To tackle this
issue, much attention has been paid to Automatic Document Summarization. The
key point in any successful document summarizer is a good document
representation. The traditional approaches based on word overlapping mostly
fail to produce that kind of representation. Word embedding, distributed
representation of words, has shown an excellent performance that allows words
to match on semantic level. Naively concatenating word embeddings makes the
common word dominant which in turn diminish the representation quality. In this
paper, we employ word embeddings to improve the weighting schemes for
calculating the input matrix of Latent Semantic Analysis method. Two
embedding-based weighting schemes are proposed and then combined to calculate
the values of this matrix. The new weighting schemes are modified versions of
the augment weight and the entropy frequency. The new schemes combine the
strength of the traditional weighting schemes and word embedding. The proposed
approach is experimentally evaluated on three well-known English datasets, DUC
2002, DUC 2004 and Multilingual 2015 Single-document Summarization for English.
The proposed model performs comprehensively better compared to the
state-of-the-art methods, by at least 1% ROUGE points, leading to a conclusion
that it provides a better document representation and a better document summary
as a result.
