Extractive Text Summarization is the process of selecting the most representative parts of a larger text without losing any key information. Recent attempts at extractive text summarization in Bengali, either relied on statistical techniques like TF-IDF or used naive sentence similarity measures like the word averaging technique. All of these strategies suffer from expressing semantic relationships correctly. Here, we propose a novel Word pair-based Gaussian Sentence Similarity (WGSS) algorithm for calculating the semantic relation between two sentences. WGSS takes the geometric means of individual Gaussian similarity values of word embedding vectors to get the semantic relationship between sentences. It compares two sentences on a word-to-word basis which rectifies the sentence representation problem faced by the word averaging method. The summarization process extracts key sentences by grouping semantically similar sentences into clusters using the Spectral Clustering algorithm. After clustering, we use TF-IDF ranking to pick the best sentence from each cluster. The proposed method is validated using four different datasets, and it outperformed other recent models by 43.2\% on average ROUGE scores (ranging from 2.5\% to 95.4\%). It is also experimented on other low-resource languages i.e. Turkish, Marathi, and Hindi language, where we find that the proposed method performs as similar as Bengali for these languages. In addition, a new high-quality Bengali dataset is curated which contains 250 articles and a pair of summaries for each of them. We believe this research is a crucial addition to Bengali Natural Language Processing (NLP) research and it can easily be extended into other low-resource languages. We made the implementation of the proposed model and data public on \href{https://github.com/FMOpee/WGSS}{https://github.com/FMOpee/WGSS}.

翻译：抽取式文本摘要是在不丢失任何关键信息的前提下，从较长文本中选择最具代表性部分的过程。近期在孟加拉语抽取式文本摘要方面的尝试，要么依赖如TF-IDF等统计技术，要么采用如词向量平均技术这类简单的句子相似度度量方法。这些策略均难以准确表达语义关系。本文提出一种新颖的基于词对的高斯句子相似度算法，用于计算两个句子之间的语义关系。WGSS通过计算词嵌入向量各自高斯相似度值的几何平均数，来获取句子间的语义关联。该算法以词对词的方式比较两个句子，从而修正了词平均方法面临的句子表示问题。摘要生成过程通过使用谱聚类算法将语义相似的句子分组为簇来提取关键句。聚类后，我们采用TF-IDF排序从每个簇中选取最佳句子。所提方法在四个不同数据集上进行了验证，其平均ROUGE分数（范围从2.5%到95.4%）比其他最新模型平均高出43.2%。该方法还在其他低资源语言（即土耳其语、马拉地语和印地语）上进行了实验，我们发现所提方法在这些语言上的表现与在孟加拉语上相似。此外，我们整理了一个新的高质量孟加拉语数据集，包含250篇文章及每篇文章对应的一对摘要。我们相信这项研究是对孟加拉语自然语言处理研究的重要补充，且可轻松扩展至其他低资源语言。我们将所提模型的实现代码及数据公开于\href{https://github.com/FMOpee/WGSS}{https://github.com/FMOpee/WGSS}。