عنوان

Query Focused Abstractive Summarization Using BERTSUM Model

شماره

TL53912

زبان متن نوشتاري يا گفتاري و مانند آن

انگلیسی

عنوان اصلي

Query Focused Abstractive Summarization Using BERTSUM Model

نام عام مواد

[Thesis]

نام نخستين پديدآور

Abdullah, Deen Mohammad

نام ساير پديدآوران

Chali, Yllias

نام ناشر، پخش کننده و غيره

University of Lethbridge (Canada)

تاریخ نشرو بخش و غیره

2020

متن يادداشت

78 p.

جزئيات پايان نامه و نوع درجه آن

M.Sc.

کسي که مدرک را اعطا کرده

University of Lethbridge (Canada)

امتياز متن

2020

متن يادداشت

In Natural Language Processing, researchers find many challenges on Query Focused Abstractive Summarization (QFAS), where Bidirectional Encoder Representations from Transformers for Summarization (BERTSUM) can be used for both extractive and abstractive summarization. As there is few available datasets for QFAS, we have generated queries for two publicly available datasets, CNN/Daily Mail and Newsroom, according to the context of the documents and summaries. To generate abstractive summaries, we have applied two different approaches, which are Query focused Abstractive and Query focused Extractive then Abstractive summarizations. In the first approach, we have sorted the sentences of the documents from the most query-related sentences to the less query-related sentences, and in the second approach, we have extracted only the query related sentences to fine-tune the BERTSUM model. Our experimental results show that both of our approaches show good results on ROUGE metric for CNN/Daily Mail and Newsroom datasets.

اصطلاح موضوعی

Artificial intelligence

اصطلاح موضوعی

Computer science

اصطلاح موضوعی

Engineering

مستند نام اشخاص تاييد نشده

Abdullah, Deen Mohammad

مستند نام اشخاص تاييد نشده

Chali, Yllias

مستند نام تنالگان تاييد نشده

University of Lethbridge (Canada)

نام الکترونيکي

فرمت انتشار

p

نوع ماده

[Thesis]

کد کاربرگه

276903

سطح دسترسي

a

تكميل شده

Y