An Automated Literature Review Generation Method Based on Adaptive Citation Verification and Two-Stage Fusion Rerank

doi:10.11871/jfdc.issn.2096-742X.2026.05.014

Abstract

Abstract:

[Objective] With the exponential growth of academic publications, manual writing of high-quality literature reviews has become increasingly time-consuming and prone to omissions and biases. In recent years, some studies have attempted to leverage large language models (LLMs) to automate the review-writing process and improve efficiency. However, challenges remain in achieving comprehensive coverage of high-quality references and ensuring citation accuracy compared to human-authored reviews. [Methods] To address these limitations, this paper proposes an automated literature review generation method based on adaptive citation verification and two-stage fusion reranking. First, query expansion is used to obtain topic descriptions that are highly relevant to the input theme. The topic descriptions are then employed to retrieve related documents. Second, a two-stage fusion reranking mechanism is designed to identify academic papers with stronger topic relevance. Finally, after generating the outline and main text, an adaptive citation verification mechanism dynamically validates and refines citations to significantly enhance citation accuracy. [Results] To evaluate the effectiveness of the proposed method, we constructed a domain knowledge base comprising 104,321 papers across three domains: sustainable development, computer science, and biomedicine, and conducted evaluations from three dimensions: citation quality, content quality, and reference quality. Experimental results demonstrate that our method outperforms existing approaches (e.g., AutoSurvey) in both literature coverage and citation accuracy, thereby substantially improving the academic reliability of automated literature reviews.

Key words: large language model (LLM), automated literature review generation, retrieval-augmented generation (RAG), long text generation

GONG Houhua, LIU Zihan, ZHANG Yunfei, LI Jianhui, PIAO Yingchao, PEI Changhua. An Automated Literature Review Generation Method Based on Adaptive Citation Verification and Two-Stage Fusion Rerank[J]. Frontiers of Data and Computing, 2026, 8(5): 177-189, https://cstr.cn/32002.14.jfdc.CN10-1649/TP.2026.05.014.

Figures/Tables 8

Fig.1

Algorithm1: The pipline of our method

Input: 用户输入主题T;论文数据库D;
OutPut: 文献综述生成结果F
// 双阶段融合重排
Function TwoStageFusionRerank(P)：
${\mathit{r}}_{\mathit{t}\mathit{i}\mathit{m}\mathit{e}}\leftarrow $RankByTime(P);
${\mathit{r}}_{\mathit{c}\mathit{i}\mathit{t}\mathit{e}}\leftarrow $RankByCitations(P);
${\mathit{r}}_{\mathit{V}\mathit{e}\mathit{c}}\leftarrow $RankByVector(P);
${\mathit{r}}_{\mathit{f}\mathit{u}\mathit{s}\mathit{e}\mathit{d}}\leftarrow $FuseByAvgInverseRank(r_time,r_cite,r_Vec);
Return LLMRerank(r_fused);
Begin
// 阶段一：查询扩展与初始检索
$\mathit{q}\mathit{u}\mathit{e}\mathit{r}\mathit{y}\leftarrow $QueryExpand(T);
${\mathit{P}}_{\mathit{i}\mathit{n}\mathit{i}\mathit{t}}\leftarrow $Retrieve(query,D);
// 阶段二：一级与二级大纲生成
基于P_init生成一级大纲集合$\left\{{\mathit{O}}_{\mathit{i}}\right\}$;
for每个一级大纲O_ido
${\mathit{P}}_{\mathit{i}}\leftarrow $Retrieve(d_i,D);
${\mathit{P}}_{\mathit{i}}^{\mathit{r}\mathit{e}\mathit{r}\mathit{a}\mathit{n}\mathit{k}\mathit{e}\mathit{d}}\leftarrow \mathit{T}\mathit{w}\mathit{o}\mathit{S}\mathit{t}\mathit{a}\mathit{g}\mathit{e}\mathit{F}\mathit{u}\mathit{s}\mathit{i}\mathit{o}\mathit{n}\mathit{R}\mathit{e}\mathit{r}\mathit{a}\mathit{n}\mathit{k}\left({\mathit{P}}_{\mathit{i}}\right)$;
基于P_i^reranked生成对应二级大纲集合$\left\{{\mathit{O}}_{\mathit{i}\mathit{j}}\right\}$;
end for
// 阶段三：章节内容并行生成
for 每个二级大纲O_ij（并行）do
for子章节描述d_ijdo
${\mathit{P}}_{\mathit{i}\mathit{j}}\leftarrow $Retrieve(d_ij,D);
${\mathit{P}}_{\mathit{i}\mathit{j}}^{\mathit{r}\mathit{e}\mathit{r}\mathit{a}\mathit{n}\mathit{k}\mathit{e}\mathit{d}}\leftarrow $TwoStageFusionRerank(P_ij);
基于P_ij^reranked生成子章节内容C_ij;
end for
end for
${\mathit{F}}_{\mathit{d}\mathit{r}\mathit{a}\mathit{f}\mathit{t}}\leftarrow $MergeSections(C_ij);
// 阶段四：自适应引用验证
$\mathit{S},{\mathit{P}}_{\mathit{r}\mathit{e}\mathit{l}\mathit{a}\mathit{t}\mathit{e}\mathit{d}}\leftarrow \mathit{E}\mathit{x}\mathit{t}\mathit{r}\mathit{a}\mathit{c}\mathit{t}\mathit{C}\mathit{i}\mathit{t}\mathit{e}\mathit{d}\mathit{S}\mathit{e}\mathit{n}\mathit{t}\mathit{e}\mathit{n}\mathit{c}\mathit{e}\mathit{s}\left({\mathit{F}}_{\mathit{d}\mathit{r}\mathit{a}\mathit{f}\mathit{t}}\right)$;
for 每个句子$\mathit{s}\in \mathit{S}$及其对应文献 $\mathit{p}\in {\mathit{P}}_{\mathit{r}\mathit{e}\mathit{l}\mathit{a}\mathit{t}\mathit{e}\mathit{d}}$do
for $\mathit{t}\leftarrow 1$ to K do
check$\leftarrow $CheckCitationConsistency(s,p);
if check==true then
break;
${\mathit{P}}_{\mathit{r}\mathit{e}\mathit{f}}\leftarrow $Retrieve(s,D);
$\mathit{s}\leftarrow $RefineSentenceWithEvidence(s,P_ref,LLM);
end for
end for
$\mathit{F}\leftarrow $MergeSentences(F_draft,S);
Return F;
End

Table 1

Table 2

Fig.2

Table 3

Fig.3

Fig.4

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Topic	文章类型	Methods	GenerationRecall	Info. density	Structure	Relevance	Coverage	Claim Recall	Claim Precision
SDG	研究型	本方法	0.22	5.70	95.17	92.29	88.84	0.86	0.84
	研究型	AutoSurvey	0.13	5.56	93.33	92.00	87.00	0.44	0.43
	综述型	本方法	0.14	5.64	95.33	92.08	87.67	0.92	0.88
	综述型	AutoSurvey	0.05	5.34	95.00	92.00	87.50	0.53	0.52
CS	研究型	本方法	0.22	6.04	94.17	92.00	88.67	0.95	0.89
	研究型	AutoSurvey	0.15	6.12	95.00	92.08	88.17	0.59	0.58
	综述型	本方法	0.12	5.98	94.83	92.08	89.50	0.94	0.90
	综述型	AutoSurvey	0.09	5.87	94.17	92.00	89.50	0.60	0.59
BioMedicine	研究型	本方法	0.41	6.27	95.20	92.00	88.40	0.92	0.91
	研究型	AutoSurvey	0.47	6.24	95.80	92.70	90.00	0.46	0.45
	综述型	本方法	0.28	6.00	95.33	87.00	85.67	0.82	0.80
	综述型	AutoSurvey	0.29	5.89	94.33	92.00	85.17	0.48	0.48

领域	文章类型	Methods	Retrieve Precision	Retrieve Recall	Generation Precision	Generation Recall
SDG	研究型	本方法	0.07	0.26	0.08	0.22
	研究型	AutoSurvey	0.05	0.16	0.07	0.13
	综述型	本方法	0.18	0.15	0.15	0.14
	综述型	AutoSurvey	0.13	0.06	0.14	0.05
	研究型	本方法	0.06	0.24	0.08	0.22
CS	研究型	AutoSurvey	0.04	0.21	0.03	0.15
	综述型	本方法	0.34	0.18	0.33	0.12
	综述型	AutoSurvey	0.28	0.09	0.30	0.09
BioMedicine	研究型	本方法	0.06	0.43	0.12	0.41
	研究型	AutoSurvey	0.10	0.51	0.14	0.47
	综述型	本方法	0.33	0.42	0.47	0.28
	综述型	AutoSurvey	0.29	0.3	0.43	0.29

Methods	Retrieve Recall	Generation Recall	Claim Recall	Claim Precision
本方法	0.28	0.23	0.88	0.87
本方法w/o QE	0.23	0.19	0.87	0.85
本方法w/o TR	0.26	0.22	0.9	0.89
本方法w/o ACV	0.28	0.23	0.56	0.54