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...

Multi-document summarization (MDS) aims to generate a summary for number of related documents. We propose HGSum — an MDS model that extends encoder-decoder architecture incorporate heterogeneous graph represent different semantic units (e.g., words and sentences) the This contrasts with existing models which do not consider edge types graphs as such capture diversity relationships in To preserv...

The purpose of text summarization is to compress a document into summary containing key information. abstract approaches are challenging tasks, it necessary design mechanism effectively extract salient information from the source text, and then generate summary. However, most existing difficult capture global semantics, ignoring impact on obtaining important content. To solve this problem, pape...

Due to the success of pre-trained language model (PLM), existing PLM-based summarization models show their powerful generative capability. However, these are trained on general-purpose datasets, leading generated summaries failing satisfy needs different readers. To generate with topics, many efforts have been made topic-focused summarization. works a summary only guided by prompt comprising to...

Source code summarization aims to generate concise descriptions for snippets in a natural language, thereby facilitates program comprehension and software maintenance. In this paper, we propose novel approach– GSCS –to automatically summaries Java methods, which leverages both semantic structural information of the snippets. To end, utilizes Graph Attention Networks process tokenized abstract s...

Current graph-based approaches to automatic text summarization, such as LexRank and TextRank, assume a static graph which does not model how the input texts emerge. A suitable evolutionary text graph model may impart a better understanding of the texts and improve the summarization process. We propose a timestamped graph (TSG) model that is motivated by human writing and reading processes, and ...