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 https://github.com/FMOpee/WGSS.

翻译：抽取式文本摘要是从较长文本中选取最具代表性的部分而不丢失关键信息的过程。近期孟加拉语抽取式文本摘要的尝试，要么依赖TF-IDF等统计技术，要么采用词向量平均等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。该算法以逐词对比的方式处理两个句子，从而修正了词向量平均方法面临的句子表示问题。摘要生成过程通过谱聚类算法将语义相似的句子分组形成簇来提取关键句。聚类完成后，我们采用TF-IDF排序从每个簇中选取最优句子。所提方法在四个不同数据集上得到验证，其平均ROUGE分数较其他最新模型平均提升43.2%（提升幅度介于2.5%至95.4%之间）。该算法还在土耳其语、马拉地语和印地语等其他低资源语言上进行了实验，结果表明其在这些语言上的表现与孟加拉语相当。此外，本研究构建了一个包含250篇文章且每篇文章配备双摘要的高质量孟加拉语数据集。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，并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与数据公开于https://github.com/FMOpee/WGSS。