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Posted: 15 Apr 2022 04:00

“Summarization” April 2022 — summary from PubMed and Astrophysics Data System

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“Summarization” April 2022 — summary from PubMed and Astrophysics Data System 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.


PubMed - summary generated by Brevi Assistant


We consider the issue of instantly generating a narrative biomedical evidence summary from multiple trial reports. We evaluate modern neural models for abstractive summarization of relevant post abstracts from systematic testimonials formerly performed by participants of the Cochrane cooperation, using the authors conclusions area of the evaluation abstract as our target.

Searching for health info online is becoming normal for more and many more consumers everyday, That makes the need for reliable and reputable question answering systems much more pushing.

We developed an abstractive question summarization model that leverages the semantic analysis of a question via recognition of clinical entities, which enables the generation of useful summaries. In this paper, we recommend a dynamic graph modeling technique to learn spatial-temporal representations for video summarization. Then, we construct a temporal graph by utilizing the aggregated representations of spatial graphs.

Patient Electronic Health Records normally contain a substantial amount of data, which can lead to information overload for clinicians, especially in high-throughput areas like radiology. This research offers a novel strategy for the curation of clinician EHR data choice information in the direction of the ultimate goal of offering robust EHR summarization. Abstractive summarization models can generate summary auto-regressively, however the top quality is typically impacted by the noise in the text. The speculative outcomes show that our model accomplishes the SOTA result on CORD-19 dataset and exceeds the relevant baseline models on the PubMed Abstract dataset.


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Astrophysics Data System - summary generated by Brevi Assistant


The Conformer model is an excellent style for speech recognition modeling that successfully uses the hybrid losses of connectionist temporal category and attention to educate model criteria. To boost the deciphering effectiveness of Conformer, we suggest a novel connectionist temporal summarization method that decreases the variety of structures required for the interest decoder fed from the acoustic sequences produced by the encoder, therefore decreasing procedures. One of the most advanced abstractive dialogue summarizers does not have generalization capacity on new domains and the existing looks into domain adjustment in summarization normally depend on massive pre-trainings. We carry out zero-shot experiments and develop domain adjustment criteria on two multi-domain dialogue summarization datasets, TODSum and QMSum. Manageable summarization intends to give summaries that take into account user-specified aspects and preferences to much better aid them with their info need, instead of the standard summarization setup which constructs a solitary common summary of a document. Regardless of recent renovations in abstractive summarization, most existing strategies generate summaries that are not factually constant with the resource document, drastically limiting their count on and usage in real-world applications. FactGraph encodes such graphs utilizing a graph encoder enhanced with structure-aware adapters to catch communications amongst the ideas based on the graph connectivity, in addition to text representations using an adapter-based text encoder.

Multimedia summarization with multimodal outcome can play an essential role in real-world applications, i. E., Instantly creating cover pictures and titles for newspaper articles or offering intros to on internet videos. We assessed MHMS on 3 recent multimodal datasets and showed the efficiency of our technique in creating high-quality multimodal summaries.


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


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