Personalized Multi-document Text Summarization using Deep Learning Techniques-Reference-Cited by-同舟云学术

Personalized Multi-document Text Summarization using Deep Learning Techniques

Published:2023 Issue: Volume:218 Page:1220-1228
ISSN:1877-0509
Container-title:Procedia Computer Science
language:en
Short-container-title:Procedia Computer Science

Author:

Veningston K,Venkateswara Rao P V,Ronalda M

Publisher

Elsevier BV

Subject

General Engineering

Reference22 articles.

1. Multi-news: a large scale multi-document summarization dataset and abstractive hierarchical model;Fabbri,2019

2. Tal Baumel, Matan Eyal, Michael Elhadad. (2018) “Query focused abstractive summarization: Incorporating query relevance, multi-document coverage, and summary length constraints into seq2seq models.” arXiv.

3. Leveraging Graph to Improve Abstractive Multi-Document Summarization;Li;Junping Du,2020

4. Abstractive multi-document summarization with semantic information extraction;Li,2015

5. ERNIE 2.0: A continual pre-training framework for language understanding;Sun,2019

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3. Research Paper Summarization Using Extractive Approach;Lecture Notes in Networks and Systems;2024

4. New Text Classification Strategy Based on a Word Embedding and Noise-Words Removal;2023 24th International Arab Conference on Information Technology (ACIT);2023-12-06