Centroid-based Text Summarization through Compositionality of Word Embeddings
Gaetano Rossiello, Pierpaolo Basile, Giovanni Maria Semeraro · 2017
The textual similarity is a crucial aspect for many extractive text summarization methods.A bag-of-words representation does not allow to grasp the semantic relationships between concepts when comparing strongly related sentences with no words in common.To overcome this issue, in this paper we propose a centroidbased method for text summarization that exploits the compositional capabilities of word embeddings.The evaluations on multi-document and multilingual datasets prove the effectiveness of the continuous vector representation of words compared to the bag-of-words model.Despite its simplicity, our method achieves good performance even in comparison to more complex deep learning models.Our method is unsupervised and it can be adopted in other summarization tasks.