SCIRGC: Multi-Granularity Citation Recommendation and Citation Sentence Preference Alignment

• URL: http://arxiv.org/abs/2505.20103v2
• Date: Tue, 27 May 2025 14:05:49 GMT
• Title: SCIRGC: Multi-Granularity Citation Recommendation and Citation Sentence Preference Alignment
• Authors: Xiangyu Li, Jingqiang Chen,
• Abstract summary: We propose the SciRGC framework, which aims to automatically recommend citation articles and generate citation sentences for citation locations within articles.<n>The framework addresses two key challenges in academic citation generation: 1) how to accurately identify the author's citation intent and find relevant citation papers, and 2) how to generate high-quality citation sentences that align with human preferences.
• Score: 2.0383262889621867
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Citations are crucial in scientific research articles as they highlight the connection between the current study and prior work. However, this process is often time-consuming for researchers. In this study, we propose the SciRGC framework, which aims to automatically recommend citation articles and generate citation sentences for citation locations within articles. The framework addresses two key challenges in academic citation generation: 1) how to accurately identify the author's citation intent and find relevant citation papers, and 2) how to generate high-quality citation sentences that align with human preferences. We enhance citation recommendation accuracy in the citation article recommendation module by incorporating citation networks and sentiment intent, and generate reasoning-based citation sentences in the citation sentence generation module by using the original article abstract, local context, citation intent, and recommended articles as inputs. Additionally, we propose a new evaluation metric to fairly assess the quality of generated citation sentences. Through comparisons with baseline models and ablation experiments, the SciRGC framework not only improves the accuracy and relevance of citation recommendations but also ensures the appropriateness of the generated citation sentences in context, providing a valuable tool for interdisciplinary researchers.

Related papers

• In-depth Research Impact Summarization through Fine-Grained Temporal Citation Analysis [52.42612945266194]
  We propose a new task: generating nuanced, expressive, and time-aware impact summaries.<n>We show that these summaries capture both praise (confirmation citations) and critique (correction citations) through the evolution of fine-grained citation intents.
  arXiv  Detail & Related papers  (2025-05-20T19:11:06Z)
• SelfCite: Self-Supervised Alignment for Context Attribution in Large Language Models [51.90867482317985]
  SelfCite is a self-supervised approach to generate fine-grained, sentence-level citations for statements in generated responses.<n>The effectiveness of SelfCite is demonstrated by increasing citation F1 up to 5.3 points on the LongBench-Cite benchmark.
  arXiv  Detail & Related papers  (2025-02-13T18:55:13Z)
• Citation Recommendation based on Argumentative Zoning of User Queries [7.596930973436683]
  argumentative zoning is to identify the argumentative and rhetorical structure in scientific literature.<n>In this paper, a multi-task learning model is built for citation recommendation and argumentative zoning classification.
  arXiv  Detail & Related papers  (2025-01-30T12:08:00Z)
• CiteBART: Learning to Generate Citations for Local Citation Recommendation [0.138120109831448]
  This paper introduces citation-specific pre-training within an encoder-decoder architecture.<n>Author-date citation tokens are masked to learn to reconstruct them to fulfill local citation recommendation (LCR)<n>CiteBART-Global achieves state-of-the-art performance on LCR benchmarks except for the FullTextPeerRead dataset.
  arXiv  Detail & Related papers  (2024-12-23T12:58:30Z)
• ALiiCE: Evaluating Positional Fine-grained Citation Generation [54.19617927314975]
  We propose ALiiCE, the first automatic evaluation framework for fine-grained citation generation. Our framework first parses the sentence claim into atomic claims via dependency analysis and then calculates citation quality at the atomic claim level. We evaluate the positional fine-grained citation generation performance of several Large Language Models on two long-form QA datasets.
  arXiv  Detail & Related papers  (2024-06-19T09:16:14Z)
• Contextualizing Generated Citation Texts [11.531517736126657]
  We propose a simple modification to the citation text generation task. The generation target is not only the citation itself, but the entire context window, including the target citation.
  arXiv  Detail & Related papers  (2024-02-28T05:24:21Z)
• CiteBench: A benchmark for Scientific Citation Text Generation [69.37571393032026]
  CiteBench is a benchmark for citation text generation. We make the code for CiteBench publicly available at https://github.com/UKPLab/citebench.
  arXiv  Detail & Related papers  (2022-12-19T16:10:56Z)
• Controllable Citation Sentence Generation with Language Models [11.186252009101077]
  We propose to integrate the manuscript context, the context of the referenced paper, and the desired control attributes into a structured template and use it to fine-tune a language model (LM) via next-token prediction. The proposed workflow harmoniously combines citation attribute suggestion and conditional citation generation into one LM, allowing for better user control.
  arXiv  Detail & Related papers  (2022-11-14T01:54:08Z)
• Towards generating citation sentences for multiple references with intent control [86.53829532976303]
  We build a novel generation model with the Fusion-in-Decoder approach to cope with multiple long inputs. Experiments demonstrate that the proposed approaches provide much more comprehensive features for generating citation sentences.
  arXiv  Detail & Related papers  (2021-12-02T15:32:24Z)
• Citations are not opinions: a corpus linguistics approach to understanding how citations are made [0.0]
  Key issue in citation content analysis is looking for linguistic structures that characterize distinct classes of citations. In this study, we start with a large sample of a pre-classified citation corpus, 2 million citations from each class of the scite Smart Citation dataset. By generating comparison tables for each citation type, we present a number of interesting linguistic features that uniquely characterize citation type.
  arXiv  Detail & Related papers  (2021-04-16T12:52:27Z)
• Enhancing Scientific Papers Summarization with Citation Graph [78.65955304229863]
  We redefine the task of scientific papers summarization by utilizing their citation graph. We construct a novel scientific papers summarization dataset Semantic Scholar Network (SSN) which contains 141K research papers in different domains. Our model can achieve competitive performance when compared with the pretrained models.
  arXiv  Detail & Related papers  (2021-04-07T11:13:35Z)
• Context-Based Quotation Recommendation [60.93257124507105]
  We propose a novel context-aware quote recommendation system. It generates a ranked list of quotable paragraphs and spans of tokens from a given source document. We conduct experiments on a collection of speech transcripts and associated news articles.
  arXiv  Detail & Related papers  (2020-05-17T17:49:53Z)
• Citation Recommendation: Approaches and Datasets [20.47628019708079]
  Citation recommendation describes the task of recommending citations for a given text. In recent years, several approaches and evaluation data sets have been presented. No literature survey has been conducted explicitly on citation recommendation.
  arXiv  Detail & Related papers  (2020-02-17T13:59:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.