  This work describes our two approaches for the background linking task of
TREC 2020 News Track. The main objective of this task is to recommend a list of
relevant articles that the reader should refer to in order to understand the
context and gain background information of the query article. Our first
approach focuses on building an effective search query by combining weighted
keywords extracted from the query document and uses BM25 for retrieval. The
second approach leverages the capability of SBERT (Nils Reimers et al.) to
learn contextual representations of the query in order to perform semantic
search over the corpus. We empirically show that employing a language model
benefits our approach in understanding the context as well as the background of
the query article. The proposed approaches are evaluated on the TREC 2018
Washington Post dataset and our best model outperforms the TREC median as well
as the highest scoring model of 2018 in terms of the nDCG@5 metric. We further
propose a diversity measure to evaluate the effectiveness of the various
approaches in retrieving a diverse set of documents. This would potentially
motivate researchers to work on introducing diversity in their recommended
list. We have open sourced our implementation on Github and plan to submit our
runs for the background linking task in TREC 2020.
