Persian Text Summarization using Sparse Coding with Neural Text Representation

The progress of communications over internet media such as social media and messengers has led to the production of large amount of textual data. This kind of information contains a lot of valuable knowledge and can be used to improve the performance of other NLP[1] tasks. There are several ways to use such information, one of which is text summarization Summarizing textual information can extract the main content of text within a short time. In this paper, we propose an approach for extractive summarization on Persian texts by using sentences embedding and a sparse coding framework. Most previous works focuses on text’s sentences individually which may not consider the hidden structure patterns between them. In this paper, our proposed approach can consider the relations between the text’s sentences by using three main criteria, namely coverage, diversity and sparsity, when selecting the summary sentences. By considering these criteria, we select sentences that can reconstruct the whole text with least reconstruction error.  The proposed approach is evaluated on Persian dataset Pasokh and achieved 10.02% and 8.65% improvement compared to the state-of-the-art methods in rouge-1 and rouge-2 f-scores, respectively. We show that considering semantic relations among the text’s sentences can lead us to better sentence summarization.