With the noteworthy expansion of textual data sources in recent years, easy, quick, and precise text processing has become a challenge for key qualifiers. Automatic summarization is process squeezing documents into shorter summaries to facilitate verification their basic contents, which must be completed without losing vital information features. The most difficult retrieval task summarization,...

In today’s world, the rapid growth of textual data on internet sites & online resources makes it challenging for human being to assimilate essential information. To handle such issues, text summarization (TS) plays an important role. Through TS process, a shorter version original content is generated preserve relevant This study suggests quantitative assessment models single and multi-document ...

Text Summarization is a process where huge text file converted into summarized version which will preserve the original meaning and context. The main aim of any summarization to provide accurate precise summary. One approach use sentence ranking algorithm. This comes under extractive summarization. Here, graph based algorithm used rank sentences in then top k-scored are included most widely dec...

Unlike extractive summarization, abstractive summarization has to fuse different parts of the source text, which inclines to create fake facts. Our preliminary study reveals nearly 30% of the outputs from a state-of-the-art neural summarization system suffer from this problem. While previous abstractive summarization approaches usually focus on the improvement of informativeness, we argue that ...

Developed so far, multi-document summarization has reached its bottleneck due to the lack of sufficient training data and diverse categories of documents. Text classification just makes up for these deficiencies. In this paper, we propose a novel summarization system called TCSum, which leverages plentiful text classification data to improve the performance of multi-document summarization. TCSu...

Sentence Connectivity is a textual characteristic that may be incorporated intelligently for the selection of sentences of a well meaning summary. However, the existing summarization methods do not utilize its potential fully. The present paper introduces a novel method for singledocument text summarization. It poses the text summarization task as an optimization problem, and attempts to solve ...

Summarization is an important challange in natural language processing. Deep learning methods, however, have not been widely used in text summarization, although neural networks have been proved to be powerful in natural language processing. In this paper, an encoder-decoder neural network model is applied to text summarization, as an important step toward this task. Besides, a hierarchical mod...

ive summarization is one of the main goals of text summarization research, but also one of its greatest challenges. The authors of a recent literature review (Lloret and Palomar 2012) even conclude that “abstractive paradigms [...] will become one of the main challenges to solve” in text summarization. In building an abstractive summarization system, however, it is often hard to imagine where t...