Similarity versus relatedness: A novel approach in extractive Persian document summarisation

Abstract

Automatic text summarisation is the process of creating a summary from one or more documents by eliminating the details and preserving the worthwhile information. This article presents a single/multi-document summariser using a novel clustering method for creating summaries. First, a feature selection phase is employed. Then, FarsNet, the Persian WordNet, is utilised to extract the semantic information of words. Therefore, the input sentences are categorised into three main clusters: similarity, relatedness and coherency. Each similarity cluster contains similar sentences to its core, while each relatedness cluster contains sentences that are related (but not similar) to its core. The coherency clusters show the sentences that should be kept together to preserve the coherency of the summary. Finally, the centroid of each similarity cluster having the most feature score is added to an empty summary. The summary is enlarged by including related sentences from relatedness clusters and excluding similar sentences to its content iteratively. Coherency clusters are applied to the created summary in the last step. The proposed method has been compared with three known existing text summarisation systems and techniques for the Persian language: FarsiSum, Parsumist and Ijaz. Our proposed method leads to improvement in experimental results on different measurements including precision, recall, F-measure, ROUGE-N and ROUGE-L.

Conceptual Persian Text Summarizer: A New Model in Continuous Vector Space

“…For example, the length of the summary FarsiSum produces has a significant difference with the requested compression ratio percentage. According to previous studies [29], [35], the results of our proposed method on Pasokh corpus are much higher than the results obtained by FarsiSum summarizer.…”

“…• Shafiee and Shamsfard [29] proposed a single/multi-document summarizer using a novel clustering method to generate text summaries. It consists of three phases: First, a feature selection phase is employed.…”

“…Shafiee and Shamsfard [29] proposed an approach in extractive single-document Persian summarization in 2017.…”

Unsupervised extractive multi-document summarization method based on transfer learning from BERT multi-task fine-tuning

“…Extractive text summarization methods identify the most salient sentences of a document and then subsequently concatenate them as they appear in the original document to create the final summary. Traditional text summarization methods are rule-based; they rely on hand-crafted features and expert knowledge [25][26][27][28][29]. With the recent advancements of neural network architectures, text summarization methods based on deep neural networks have received much attention and have achieved promising results.…”