Topic Based Machine Learning Summarizer

Lukyamuzi, Andrew; Ngubiri, John; Okori, Washington

doi:10.1109/isc246665.2019.9071737

Cited by 2 publications

(1 citation statement)

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“…UNL is used for text summary because in his native language the user can receive a summary. Lukyamuzi et al (2019) developed A summary that relies on an improved classification domain-based corpus. Three contributions are made: (1) a synthesis is extended to cover the search for a food security text, (2) a proper corpus is used to support an up-to -date summary, and (3) a less reliable extractive summary can be made in certain conditions.…”

Section: Literature Reviewmentioning

confidence: 99%

Abstractive method-based Text Summarization using Bidirectional Long Short-Term Memory and Pointer Generator Mode

Tripathy¹,

Sharmila

2023

JART

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With the rise of the Internet, we now have a lot of information at our disposal. We 're swamped from many sources — news, social media, to name a few, office emails. This paper addresses the problem of reading through such extensive information by summarizing it using text summarizer based on Abstractive Summarization using deep learning models, i.e. using bidirectional Long Short-Term Memory (LSTM) networks and Pointer Generator mode. The LSTM model (which is a modification of the Recurrent Neural Network) is trained and tested on the Amazon Fine Food Review dataset using the Bahadau Attention Model Decoder with the use of Conceptnet Numberbatch embeddings that are very similar and better to GloVe. Pointer Generator mode is trained and tested by the CNN / Daily Mail dataset and the model uses both Decoder and Attention inputs. But due 2 major problems in LSTM model like the inability of the network to copy facts and repetition of words the second method is, i.e., Pointer Generator mode is used. This paper in turn aims to provide an analysis on both the models to provide a better understanding of the working of the models to enable to create a strong text summarizer. The main purpose is to provide reliable summaries of datasets or uploaded files, depending on the choice of the user. Unnecessary sentences will be rejected in order to obtain the most important sentences.

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Section: Literature Reviewmentioning

confidence: 99%