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Experiments in Text Summarization Us...

ProQuest Information and Learning Co.

Experiments in Text Summarization Using Deep Learning.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Experiments in Text Summarization Using Deep Learning./
作者:	Bulusu, Sai Amrit.
面頁冊數:	1 online resource (70 pages)
附註:	Source: Masters Abstracts International, Volume: 57-05.
Contained By:	Masters Abstracts International57-05(E).
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9780355941708

Experiments in Text Summarization Using Deep Learning.
Bulusu, Sai Amrit.

Experiments in Text Summarization Using Deep Learning. - 1 online resource (70 pages)

Source: Masters Abstracts International, Volume: 57-05.

Thesis (M.S.)--The University of North Carolina at Charlotte, 2018.

Includes bibliographical references

Deep Learning has been the go-to tool for text summarization in the recent times. Traditional deep learning research focuses on performing abstractive text summarization without considering the user's interests to personalize the summaries.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355941708Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Experiments in Text Summarization Using Deep Learning.
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500 $a Source: Masters Abstracts International, Volume: 57-05.
500 $a Adviser: Wlodek Zadrozny.
502 $a Thesis (M.S.)--The University of North Carolina at Charlotte, 2018.
504 $a Includes bibliographical references
520 $a Deep Learning has been the go-to tool for text summarization in the recent times. Traditional deep learning research focuses on performing abstractive text summarization without considering the user's interests to personalize the summaries.
520 $a This problem motivated us to develop a deep learning based text summarization system which can curate personalized summaries. In this work, we propose an LSTM based Bi-Directional Recurrent Neural Network model to perform extractive text summarization. Our new deep learning approach focuses on personalizing the extractive summaries based on user's interests to make the summaries more intriguing to the user. We performed the experiments on CNN and Daily Mail news dataset. We also have experimented with a new set of semantic word vectors called Conceptnet Numberbatch. Out of domain evaluation was done on the Signal-Media one million news articles dataset. Experimental results on the two summarization datasets demonstrate that our models obtain results comparable to the state of the art. The personalization framework curates interesting summaries based on user's interests while retaining the important information from the source document.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Artificial intelligence. $3 559380
650 4 $a Information science. $3 561178
655 7 $a Electronic books. $2 local $3 554714
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710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a The University of North Carolina at Charlotte. $b Computer Science. $3 1182328
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856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10816330 $z click for full text (PQDT)

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