An Effective Deep Learning Approach for Extractive Text Summarization
Minh-Tuan Luu, Thanh-Huong Le, Minh-Tan Hoang · Indian Journal of Computer Science and Engineering · 2021
Nowadays, most research on extractive text summarization uses deep learning approaches as they provide better performances than the others.However, a difficulty in these approaches is the shortage of a large dataset for training summarization systems.To deal with this problem, we take advantage of contextualized word embeddings from pre-trained BERT models to produce sentence embedding vectors.These vectors are then used as the input of a Multi-Layer Perceptron classifier for sentence selection.The outputs of the Multi-Layer Perceptron classifier are processed by a Maximal Marginal Relevance algorithm to remove redundant sentences.Finally, the selected sentences are rearranged using information about sentence position in the original document to create a summary.Our proposed system is evaluated by using both English and Vietnamese datasets.Experimental results show that our system achieves promising results comparing to existing researches in this field.