A semantic space approach for automatic summarization of documents

Automatic document summarization is used to extract or generate short, but rich snippets which represent well the essential meaning and key information contained in documents. Classical approaches of extractive summarization mostly rely on bag-of-words models or a graph representation reflecting word neighbourhood information to obtain a ranking of the sentences of the document. The higher the rank, the more relevant is the sentence for the summary. Given word-embeddings have recently been shown to represent semantic meaning of individual words in natural languages, intuitively it seems to be a straightforward way to carry out such a ranking in the latent space reflecting the meaning (semantics), not just the form (syntax). Vector operations in the semantic space have been shown to be highly consistent with alternations of the meaning reflected by such operations (i.e. analogical reasoning tasks). In the present paper we show that simply cumulatively adding semantic space vectors to represent sentence level meaning already yields comparable performance to the state-of-the-art in document summarization. The additive property of semantic representations for high number of component words is considered an important and promising outcome of this research for cognitive infocommunication applications.