[Paper Review] Unsupervised Keyphrase Extraction using Sentence Embeddings

Автор: 서울대학교 산업공학과 DSBA 연구실

Загружено: 2022-03-07

Просмотров: 1060

Описание:

1. 발표자: 김도윤(석박통합과정/석사과정 2학기)

2. 논문 제목 : Simple Unsupervised Keyphrase Extraction using Sentence Embeddings (EmbedRank)

3. 논문 링크 : https://arxiv.org/pdf/1801.04470.pdf

4. 공식 github 링크 : https://github.com/swisscom/ai-resear...

5. 논문 Overview :
Sentence, Document Embedding을 처음으로 적용, semantic relatedness를 이전보다 정확히 구할 수 있음

Informativeness: 문서와 keyphrase가 연관되어 있어야 함 - 문서와 keyphrase의 임베딩 벡터의 유사도를 통해 계산

Diversity: 다양한 keyphrase가 추출되어야 Document를 고루 커버할 수 있음 – keyphrase의 임베딩 벡터 간 거리를 통해 확인

Embedding-based 계열 방법론의 시초

MMR(Maximal Marginal Relevance)을 통해 추출되는 Keyphrase의 다양성을 확보하는 과정 설명

6. 영상 내용 Overview:
Unsupervised Keyphrase Extraction의 주요 사항 및 TextRank 부터 현재의 방법론까지 방법론들의 발전 흐름 확인

7. 발표자료:
http://dsba.korea.ac.kr/seminar/?mod=...

[Paper Review] Unsupervised Keyphrase Extraction using Sentence Embeddings

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