专题:Advanced Text Analysis Techniques

This cluster of papers focuses on the automatic extraction of keywords from textual data using various techniques such as graph-based methods, unsupervised approaches, and neural networks. The research explores the application of linguistic knowledge and statistical information to improve the accuracy of keyword extraction from documents.
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BERTopic: Neural topic modeling with a class-based TF-IDF procedure

preprint Full Text OpenAlex 1416 FWCI0

Abstractive Text Summarization Using GAN

conference-abstract Full Text OpenAlex 735 FWCI0

Unified Structure Generation for Universal Information Extraction

conference-paper Full Text OpenAlex 468 FWCI53.4679

Benchmarking Large Language Models for News Summarization

article Full Text OpenAlex 414 FWCI53.928

Adapting Feature Selection Algorithms for the Classification of Chinese Texts

article Full Text OpenAlex 336 FWCI13.1041

Large language models for generative information extraction: a survey

article Full Text OpenAlex 323 FWCI47.2605

Topic modeling algorithms and applications: A survey

article Full Text OpenAlex 306 FWCI18.0038

SummaC : Re-Visiting NLI-based Models for Inconsistency Detection in Summarization

article Full Text OpenAlex 235 FWCI11.4897

BRIO: Bringing Order to Abstractive Summarization

conference-paper Full Text OpenAlex 222 FWCI29.1517

Creating Knowledge Graph of Electric Power Equipment Faults Based on BERT–BiLSTM–CRF Model

article Full Text OpenAlex 204 FWCI14.878