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Two of the most crucial issues for text summarization to generate faithful summaries are to make use of knowledge beyond text and to make use of cross-sentence relations in text. Intuitive ways for the two issues are knowledge graph (KG) and graph neural network (GNN), respectively. Entities are semantic units in text and in KG. This paper focuses on both issues by leveraging entities mentioned in text to connect GNN and KG for summarization. Firstly, entities are leveraged to construct a sentence-entity graph with weighted multi-type edges to model sentence relations, and a relational heterogeneous GNN for summarization is proposed to calculate node encodings. Secondly, entities are leveraged to link the graph to KG to collect knowledge. Thirdly, entities guide a two-step summarization framework defining a multitask selector to select salient sentences and entities, and using an entity-focused abstractor to compress the sentences. GNN is connected with KG by constructing sentence-entity graphs where entity–entity edges are built based on KG, initializing entity embeddings on KG, and training entity embeddings using entity–entity edges. The relational heterogeneous GNN utilizes both edge weights and edge types in GNN to calculate graphs with weighted multi-type edges. Experiments show the proposed method outperforms extractive baselines including the HGNN-based HGNNSum and abstractive baselines including the entity-driven SENECA on CNN/DM, and outperforms most baselines on NYT50. Experiments on sub-datasets show the density of sentence-entity edges greatly influences the performance of the proposed method. The greater the density, the better the performance. Ablations show effectiveness of the method. [ABSTRACT FROM AUTHOR]

Creating a summarized version of a text document that still conveys precise meaning is an incredibly complex endeavor in natural language processing (NLP). Abstract text summarization (ATS) is the process of using facts from source sentences and merging them into concise representations while maintaining the content and intent of the text. Manually summarizing large amounts of text are challenging and time-consuming for humans. Therefore, text summarization has become an exciting research focus in NLP. This research paper proposed an ATS model using a Transformer Technique with Self-Attention Mechanism (T2SAM). The self-attention mechanism is added to the transformer to solve the problem of coreference in text. This makes the system to understand the text better. The proposed T2SAM model improves the performance of text summarization. It is trained on the Inshorts News dataset combined with the DUC-2004 shared tasks dataset. The performance of the proposed model has been evaluated using the ROUGE metrics, and it has been shown to outperform the existing state-of-the-art baseline models. The proposed model gives the training loss minimum to 1.8220 from 10.3058 (at the starting point) up to 30 epochs, and it achieved model accuracy 48.50% F1-Score on both the Inshorts and DUC-2004 news datasets. [ABSTRACT FROM AUTHOR]

In today's world as the data on the web is increasing it becomes a challenge to identify the relevant information. Automatic text summarization (ATS) provides a significant answer to it. In this paper, fuzzy logic and shark smell optimization (SSO) based algorithm for extractive text summarization is proposed. Shark Smell Optimization has been used to assign a weight to eight different features to identify the less and more important text features of text summarization. Then, the Fuzzy Logic's inference system is utilized to generate fuzzy rules, and finally an automated summary is generated. The system generated summaries have been tested against the reference summaries from the DUC 2002, DUC 2003, DUC-2004 and TAC-11 dataset and ROUGE toolkit has been used for the evaluation of the proposed solution. Results of the proposed algorithm are compared against traditional methods and the rouge score suggested that the proposed algorithm generates better results than other methods. [ABSTRACT FROM AUTHOR]

The original article can be found online at https://doi.org/10.1007/s00521-023-08680-0.Correction to: Neural Computing and Applications (2023) 35:18553–18570https://doi.org/10.1007/s00521-023-08680-0In this article the author's name Shuaimin Li was incorrectly written as Shuimin Li.The original article has been corrected.Publisher's NoteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.By Shuaimin Li and Jungang XuReported by Author; Author [Extracted from the article]

It is important for abstractive summarization models to understand the important parts of the original document and create a natural summary accordingly. Recently, studies have been conducted to incorporate important parts of the original document during learning and have shown good performance. However, these studies are effective for explicit datasets but not implicit datasets which are relatively more abstract. This study addresses the challenge of summarizing implicit datasets, which have a lower deviation in the significance of important sentences compared to explicit datasets. A multi-task learning approach that reflects information about salient and incidental objects during the learning process was proposed. This was achieved by adding a contrastive objective to the fine-tuning process of the encoder-decoder language model. The salient and incidental parts were selected based on the ROUGE-L F1 score and their relationships were learned through triplet loss. The proposed method was evaluated using five benchmark summarization datasets, including two explicit and three implicit. The experimental results showed a greater improvement in implicit datasets, particularly for the highly abstractive XSum dataset, compared to the vanilla fine-tuning method in both the BART-base and T5-small models. [ABSTRACT FROM AUTHOR]