Multi-document summarization

About: Multi-document summarization is a research topic. Over the lifetime, 2270 publications have been published within this topic receiving 71850 citations.

Spoken document summarization and retrieval for wireless application

Abstract: For the purpose of wireless data transformation, spoken document summarization can efficiently reduce the redundant contents. This study presents a voice-activated spoken document summarization and retrieval scheme using text and speech analysis. In this method, prosody, speech recognition confidence, word significance, word trigram and semantic dependency are considered in the summarization score. A dynamic programming algorithm is used to seek the best summarization result. Experimental results indicate that the proposed approach effectively summarizes concise spoken sentences containing important words with semantic dependency.

Single document Summarization based on Clustering Coefficient and Transitivity Analysis

Abstract: Document summarization is a technique aimed to automatically extract the main ideas from electronic documents. With the fast increase of electronic documents available on the network, techniques for making efficient use of such documents become increasingly important. In this paper, we propose a novel algorithm, called TriangleSum for single document summarization based on graph theory. The algorithm builds a dependency graph for the document based on syntactic dependency relation analysis. The nodes represent words or phrases of high frequency, and edges represent dependency relations between them. Then, a modified version of clustering coefficient is used to measure the strength of connection between nodes in a graph. By identifying triangles of nodes, a part of the dependency graph can be extracted. At last, a set of key sentences that represent the main document information can be extracted.

An Integrated Approach to Web Document Summarization Using Semantic Similarity

Abstract: Multi document summarization is an automatic procedure aimed at extraction of information from multiple texts written about the same topic. The output will be a paragraph summary. Multi document summarization is very useful for presenting and organizing search results. An existing cluster-based summarization approaches utilize the clustering results to select the representative sentences in order to generate summaries. But it not considers providing the ranking for same meaning with different words or terms in the given set of documents. We proposed integrated approach that overcomes the drawback that we provide ranking for same meaning of different terms. The proposed approach aims to improve the clustering results compared to the existing methods.

Semi-extractive Multi-document Summarization via Submodular Functions

Abstract: In this paper, we propose an Extractive Maximum Coverage KnaPsack MCKP based model for query-based multi document summarization which integrates three monotone and submodular measures to detect importance of a sentence including Coverage, Relevance, and Compression. We apply an efficient scalable greedy algorithm to generate a summary which has a near optimal solution when its scoring functions are monotone nondecreasing and submodular. We use DUC 2007 dataset to evaluate our proposed method and the result shows improvement over two closely related works.

Fuzzy Bi-GRU Based Hybrid Extractive and Abstractive Text Summarization for Long Multi-documents

Abstract: As a massive amount of information is produced on the internet nowadays, the need for extracting the most useful and relevant information from that massive list is one of the most attractive research and it can be possible through a mechanism called automatic text summarization (ATS). This summarization mechanism is classified into single and multi-documents based on the number of source documents. When multiple source documents communicate similar information called multi-documents and it is the biggest challenge in the field of ATS. This motivates us to work on the long multi-documents by calculating the sentence scores using a fuzzy inference system. From the extracted sentences, the similarity or redundancy has to be removed using Bi- GRU, and then an abstractive summary need to be generated for those identified sentences has to produce. The proposed system is validated and tested using Standard datasets namely, DUC, BBC news, and CNN/daily mail. The proposed Fuzzy Bi-GRU is compared with other cutting-edge models, and empirical results indicate that it outperforms all other models in terms of ROUGE- N and L scores.