Citation recommendation (CR) models may help authors find relevant articles
at various stages of the paper writing process. Most research has dealt with
either global CR, which produces general recommendations suitable for the
initial writing stage, or local CR, which produces specific recommendations
more fitting for the final writing stages. We propose the task of
paragraph-level CR as a middle ground between the two approaches, where the
paragraph's topic sentence is taken as input and recommendations for citing
within the paragraph are produced at the output. We propose a model for this
task, fine-tune it using the quadruplet loss on the dataset of ACL papers, and
show improvements over the baselines.

提出了基于段落级别的引用推荐任务，通过 fine-tune 模型和四元组损失函数在 ACL 论文数据集上的实验，证明了该方法的优越性。

基于主题句作为查询的段落级引文推荐

Paragraph-level Citation Recommendation based on Topic Sentences as  Queries

We consider the large-scale query-document retrieval problem: given a query
(e.g., a question), return the set of relevant documents (e.g., paragraphs
containing the answer) from a large document corpus. This problem is often
solved in two steps. The retrieval phase first reduces the solution space,
returning a subset of candidate documents. The scoring phase then re-ranks the
documents. Critically, the retrieval algorithm not only desires high recall but
also requires to be highly efficient, returning candidates in time sublinear to
the number of documents. Unlike the scoring phase witnessing significant
advances recently due to the BERT-style pre-training tasks on cross-attention
models, the retrieval phase remains less well studied. Most previous works rely
on classic Information Retrieval (IR) methods such as BM-25 (token matching +
TF-IDF weights). These models only accept sparse handcrafted features and can
not be optimized for different downstream tasks of interest. In this paper, we
conduct a comprehensive study on the embedding-based retrieval models. We show
that the key ingredient of learning a strong embedding-based Transformer model
is the set of pre-training tasks. With adequately designed paragraph-level
pre-training tasks, the Transformer models can remarkably improve over the
widely-used BM-25 as well as embedding models without Transformers. The
paragraph-level pre-training tasks we studied are Inverse Cloze Task (ICT),
Body First Selection (BFS), Wiki Link Prediction (WLP), and the combination of
all three.

本文研究基于嵌入的检索模型，并探究对段落级别的预训练任务在训练强大的嵌入式 Transformer 模型上的关键作用。