skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Keyphrase Extraction in Scholarly Digital Library Search Engines
Scholarly digital libraries provide access to scientific publications and comprise useful resources for researchers who search for literature on specific subject areas. CiteSeerX is an example of such a digital library search engine that provides access to more than 10 million academic documents and has nearly one million users and three million hits per day. Artificial Intelligence (AI) technologies are used in many components of CiteSeerX including Web crawling, document ingestion, and metadata extraction. CiteSeerX also uses an unsupervised algorithm called noun phrase chunking (NP-Chunking) to extract keyphrases out of documents. However, often NP-Chunking extracts many unimportant noun phrases. In this paper, we investigate and contrast three supervised keyphrase extraction models to explore their deployment in CiteSeerX for extracting high quality keyphrases. To perform user evaluations on the keyphrases predicted by different models, we integrate a voting interface into CiteSeerX. We show the development and deployment of the keyphrase extraction models and the maintenance requirements.  more » « less
Award ID(s):
1823288
PAR ID:
10271903
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
International Conference on Web Services
Page Range / eLocation ID:
179-196
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. (, Large Scale Networking (LSN) Workshop on Huge Data: A Computing, Networking and Distributed Systems Perspective)
    The volume of scholarly data has been growing exponentially over the last 50 years. The total size of the open access documents is estimated to be 35 million by 2022. The total amount of data to be handled, including crawled documents, production repository, metadata, extracted content, and their replications, can be as high as 350TB. Academic digital library search engines face significant challenges in maintaining sustainable services. We discuss these challenges and propose feasible solutions to key modules in the digital library architecture including the document storage, data extraction, database and index. We use CiteSeerX as a case study. 
    more » « less
  2. (, Large Scale Networking (LSN) Workshop on Huge Data: A Computing, Networking and Distributed Systems Perspective)
    The volume of scholarly data has been growing exponentially over the last 50 years. The total size of the open access documents is estimated to be 35 million by 2022. The total amount of data to be handled, including crawled documents, production repository, metadata, extracted content, and their replications, can be as high as 350TB. Academic digital library search engines face significant challenges in maintaining sustainable services. We discuss these challenges and propose feasible solutions to key modules in the digital library architecture including the document storage, data extraction, database and index. We use CiteSeerX as a case study. 
    more » « less
  3. (, Challenges For Large Scale Networking (LSN) Workshop on Huge Data: A Computing, Networking and Distributed Systems Perspective)
    The volume of scholarly data has been growing exponentially over the last 50 years. The total size of the open access documents is estimated to be 35 million by 2022. The total amount of data to be handled, including crawled documents, production repository, metadata, extracted content, and their replications, can be as high as 350TB. Academic digital library search engines face signi cant challenges in maintaining sustainable services. We discuss these challenges and propose feasible solutions to key modules in the digital library architecture including the document storage, data extraction, database and index. We use CiteSeerX as a case study. 
    more » « less
  4. (, Zenodo)
    This is the train-test-validation dataset for pubmed open-access articles keyphrase extraction task. The small_* file contains the all articles that have 5to 25 extractive keyphrases (keyphrase in the article that is inside the abstract of the article).</p> 
    more » « less
  5. ; ; ; (, Proceedings of the AAAI Conference on Artificial Intelligence)
    Keyphrase generation aims to summarize long documents with a collection of salient phrases. Deep neural models have demonstrated remarkable success in this task, with the capability of predicting keyphrases that are even absent from a document. However, such abstractiveness is acquired at the expense of a substantial amount of annotated data. In this paper, we present a novel method for keyphrase generation, AutoKeyGen, without the supervision of any annotated doc-keyphrase pairs. Motivated by the observation that an absent keyphrase in a document may appear in other places, in whole or in part, we construct a phrase bank by pooling all phrases extracted from a corpus. With this phrase bank, we assign phrase candidates to new documents by a simple partial matching algorithm, and then we rank these candidates by their relevance to the document from both lexical and semantic perspectives. Moreover, we bootstrap a deep generative model using these top-ranked pseudo keyphrases to produce more absent candidates. Extensive experiments demonstrate that AutoKeyGen outperforms all unsupervised baselines and can even beat a strong supervised method in certain cases. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email