PMID- 37346502
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230701
IS  - 2376-5992 (Electronic)
IS  - 2376-5992 (Linking)
VI  - 9
DP  - 2023
TI  - Unsupervised query reduction for efficient yet effective news background linking.
PG  - e1191
LID - 10.7717/peerj-cs.1191 [doi]
LID - e1191
AB  - In this article, we study efficient techniques to tackle the news background 
      linking problem, in which an online reader seeks background knowledge about a 
      given article to better understand its context. Recently, this problem attracted 
      many researchers, especially in the Text Retrieval Conference (TREC) community. 
      Surprisingly, the most effective method to date uses the entire input news 
      article as a search query in an ad-hoc retrieval approach to retrieve the 
      background links. In a scenario where the lookup for background links is 
      performed online, this method becomes inefficient, especially if the search scope 
      is big such as the Web, due to the relatively long generated query, which results 
      in a long response time. In this work, we evaluate different unsupervised 
      approaches for reducing the input news article to a much shorter, hence 
      efficient, search query, while maintaining the retrieval effectiveness. We 
      conducted several experiments using the Washington Post dataset, released 
      specifically for the news background linking problem. Our results show that a 
      simple statistical analysis of the article using a recent keyword extraction 
      technique reaches an average of 6.2x speedup in query response time over the full 
      article approach, with no significant difference in effectiveness. Moreover, we 
      found that further reduction of the search terms can be achieved by eliminating 
      relatively low TF-IDF values from the search queries, yielding even more 
      efficient retrieval of 13.3x speedup, while still maintaining the retrieval 
      effectiveness. This makes our approach more suitable for practical online 
      scenarios. Our study is the first to address the efficiency of news background 
      linking systems. We, therefore, release our source code to promote research in 
      that direction.
CI  - (c) 2023 Essam and Elsayed.
FAU - Essam, Marwa
AU  - Essam M
AD  - Qatar University, Doha, Qatar.
FAU - Elsayed, Tamer
AU  - Elsayed T
AD  - Qatar University, Doha, Qatar.
LA  - eng
PT  - News
DEP - 20230113
PL  - United States
TA  - PeerJ Comput Sci
JT  - PeerJ. Computer science
JID - 101660598
PMC - PMC10280215
OTO - NOTNLM
OT  - Ad-hoc retrieval
OT  - Efficiency analysis
OT  - Keyword extraction
OT  - News linking
OT  - News recommendation
OT  - Query reduction
COIS- The authors declare that they have no competing interests.
EDAT- 2023/06/22 13:09
MHDA- 2023/06/22 13:10
PMCR- 2023/01/13
CRDT- 2023/06/22 09:53
PHST- 2022/08/15 00:00 [received]
PHST- 2022/11/28 00:00 [accepted]
PHST- 2023/06/22 13:10 [medline]
PHST- 2023/06/22 13:09 [pubmed]
PHST- 2023/06/22 09:53 [entrez]
PHST- 2023/01/13 00:00 [pmc-release]
AID - cs-1191 [pii]
AID - 10.7717/peerj-cs.1191 [doi]
PST - epublish
SO  - PeerJ Comput Sci. 2023 Jan 13;9:e1191. doi: 10.7717/peerj-cs.1191. eCollection 
      2023.