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Posted: 06 Feb 2022 02:00

“Multi-Document Summarization” February 2022 — summary from Crossref

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“Multi-Document Summarization” February 2022 — summary from Crossref main image

The content below is machine-generated by Brevi Technologies’ NLG model, and the source content was collected from open-source databases/integrate APIs.


Crossref - summary generated by Brevi Assistant


Increasingly, more individual remarks like Tweets are available, which commonly consist of user issues. Since discovering latent subjects for RA-MDS is a crucial action, we also present a Heterogeneous-length Text Topic Modeling to extract topics from the corpus that consists of both report and individual comments, represented as heterogeneous-length texts. Additionally, experimental results show that the recommended subject modeling method outperforms existing topic modeling formulas.

Function The purpose of this research is to develop an approach for automated construction of multi‐document summaries of sets of newspaper articles that may be obtained by a web search engine in response to a customer inquiry. It indicates that the event‐based framework is an efficient way to summarize a collection of newspaper articles reporting an occasion or a series of appropriate events.

Research limitations/implications Limited to event‐based newspaper articles only, not suitable for news critiques and other sorts of news articles.

The students and educators of the teaching-learning procedure highly depend on on-line learning systems such as E-learning, which consists of significant quantities of digital components connected to a course. This post applies the job of MDS in an E-learning context.

The purpose of this write-up is threefold: 1 layout a common graph based multi-document summarizer DSGA Dynamic Summary Generation Algorithm to produce a variable size dynamic summary of scholastic text based learning materials based upon a learner's demand; 2 examine the summary generation procedure; 3 carry out content-based and task-based evaluations on the created summary.


This can serve as an example of how to use Brevi Assistant and integrated APIs to analyze text content.


Source texts:


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The Brevi assistant is a novel way to summarize, assemble, and consolidate multiple text documents/contents.

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