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Posted: 22 Oct 2021 21:00

“Summarization” October 2021 — summary from DOAJ and Crossref

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“Summarization” October 2021 — summary from DOAJ and 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.

DOAJ - summary generated by Brevi Assistant

The paper describes a convex optimization formula for the extractive text summarization problem and a basic and scalable formula to resolve it. The optimization program is built as a convex relaxation of a intuitive however computationally difficult integer programs trouble. Using the datasets DUC 2005 and Cornell Newsroom Summarization Dataset, we have shown empirically that the formula can provide competitive outcomes for solitary document summarization and multi-document query-based summarization. In the last few years, the explainable artificial intelligence standard has gaining broad research interest. Right here, we suggest two different transformer-based methodologies manipulating the inner hierarchy of the documents to do a belief evaluation job while removing one of the most essential sentences to develop a recap as the explanation of the output. For the various other design, we employed a solitary transformer to identify the single sentences in the document and after that integrate the chance scores of each to carry out the category and afterwards construct the summary. With the remarkable growth in the number of digital papers, it is ending up being challenging to take care of the volume of details. Multi-Document Summarization is one method that aims to draw out the details from the readily available files in such a concise way that none of the vital points are missed from the summary while avoiding the redundancy of details at the very same time. Research work is offered by category and assessed to help the visitor understand the work in this field and to assist them in specifying their own research directions. With the increase in the quantity of text information in various real-life applications, automatic text-summarization systems have come to be more primary in extracting pertinent information. The services of DE encode a feasible subset of sentences to be present in the summary which is after that reviewed based upon some statistical features namely, the setting of the sentence in the document, the resemblance of a sentence with the title, size of the sentence, cohesion, readability, and coverage. In addition to these basic datasets, CNN information dataset is additionally utilized to assess the efficiency of our proposed strategy.

Source texts:

Crossref - summary generated by Brevi Assistant

Going through hundreds of remarks in order to comprehend the viewpoint of people on a particular post ingests costs a great deal of time and resources for the customer. Extractive summary generation takes advantage of the Page rank algorithm and abstractive summary generation utilizes RNN. Deep neural networks have been used effectively to extractive text summarization jobs with along with large training datasets. In this paper, we propose an extractive summarization system based on a Convolutional Neural Network and a Fully Connected network for sentence selection. The paper defines a convex optimization formulation of the extractive text summarization problem and a straightforward and scalable algorithm to solve it. Using the datasets DUC 2005 and Cornell Newsroom Summarization Dataset, we have shown empirically that the algorithm can provide competitive results for solitary document summarization and multi-document query-based summarization. Nowadays, most research carried out in the area of abstractive text summarization concentrates on neural-based models alone, without considering their combination with knowledge-based approaches that can further enhance their effectiveness. The pre-processing task is a knowledge-based method, based on ontological expertise resources, word feeling disambiguation, and named entity recognition, in addition to content generalization, that transforms ordinary text right into a general type. The traditional regularity based approach to creating multi-document extractive summary ranks sentences based on scores computed by summing up TF * IDF weights of words had in the sentences. When the distributional term resemblance action is made use of for discovering semantic term relationships, the speculative results disclose that the performance of our multi-document text summarizer is substantially improved.

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

Source texts:


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


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