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Swarm Diversity Based Text Summarization | SpringerLink
The summary creation under the condition of the redundancy and the summary length limitation is a challenge problem. The automatic text summarization system which is built based on exploiting of the advantages of different techniques in form of an integrated model could produce a good summary for the original document.
 A Survey On Hybrid Model Automatic Text Summarization
Our hybrid model is an extractive based text summarization model. Four different techniques are involved in this model: MMI, PSO, Fuzzy Logic and Neural networks. MMI concentrates on filtering the similar sentences present in the document and selecting the most diverse sentences, PSO is used for
Fuzzy evolutionary cellular learning automata model for ...
FSDH : This work proposes a fuzzy swarm diversity hybrid model for text summarization. It combines three methods: (i) MMI diversity, (ii) swarm diversity based method, and (iii) fuzzy swarm based method. • FEOM : In this work, a model is proposed based on fuzzy evolutionary optimization which performs document clustering. The sentences with ......
Semi-Automatic Indexing of Full Text Biomedical Articles
We started back with the best MMI only model: title & abstract, captions, introduction, results, discussion, other, no header. As with the title and abstract based REL indexing, the best performance is achieved when we use all 10 of the available citations for each section. Table 2 shows this result in the context of the other major model versions.
PEGASUS: A State-of-the-Art Model for Abstractive Text ...
This abstractive text summarization is one of the most challenging tasks in natural language processing, involving understanding of long passages, information compression, and language generation. The dominant paradigm for training machine learning models to do this is sequence-to-sequence (seq2seq) learning, where a neural network learns to ...
Text Generation With LSTM Recurrent Neural Networks in ...
Recurrent neural networks can also be used as generative models. This means that in addition to being used for predictive models (making predictions) they can learn the sequences of a problem and then generate entirely new plausible sequences for the problem domain. Generative models like this are useful not only to study how well a model has learned a problem, but to
Electronics | Free Full-Text | Incorporating External ...
Supervised neural network models have achieved outstanding performance in the document summarization task in recent years. However, it is hard to get enough labeled training data with a high quality for these models to generate different types of summaries in reality. In this work, we mainly focus on improving the performance of the popular unsupervised Textrank algorithm that requires no ...
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