Paper Info
Title
Leveraging Passage-level Cumulative Gain for Document Ranking
Abstract
Document ranking is one of the most studied but challenging problems in information retrieval (IR) research. A number of existing document ranking models capture relevance signals at the whole document level. Recently, more and more research has begun to address this problem from fine-grained document modeling. Several works leveraged fine-grained passage-level relevance signals in ranking models. However, most of these works focus on context-independent passage-level relevance signals and ignore the context information, which may lead to inaccurate estimation of passage-level relevance. In this paper, we investigate how information gain accumulates with passages when users sequentially read a document. We propose the context-aware Passage-level Cumulative Gain (PCG), which aggregates relevance scores of passages and avoids the need to formally split a document into independent passages. Next, we incorporate the patterns of PCG into a BERT-based sequential model called Passage-level Cumulative Gain Model (PCGM) to predict the PCG sequence. Finally, we apply PCGM to the document ranking task. Experimental results on two public ad hoc retrieval benchmark datasets show that PCGM outperforms most existing ranking models and also indicates the effectiveness of PCG signals. We believe that this work contributes to improving ranking performance and providing more explainability for document ranking.
Year
DOI
Venue
2020
10.1145/3366423.3380305
WWW '20: The Web Conference 2020 Taipei Taiwan April, 2020
Keywords
DocType
ISBN
Passage-level cumulative gain, document ranking, neural network
Conference
978-1-4503-7023-3
Citations 
PageRank 
References 
2
0.37
0
Authors
7
Name
Order
Citations
PageRank
Zhijing Wu1254.15
Jiaxin Mao216426.30
Yiqun Liu31592136.51
Jingtao Zhan4163.42
Yukun Zheng5314.02
Min Zhang61658134.93
Shaoping Ma71544126.00