diff --git a/docs/src/about.md b/docs/src/about.md
index 72f0810..4d70bf7 100644
--- a/docs/src/about.md
+++ b/docs/src/about.md
@@ -1,6 +1,6 @@
 # About
 
-ANOVAapprox.jl has been developed during the research for several papers; see ... .
+ANOVAapprox.jl has been developed during the research for several papers.
 It is currently maintained by [Michael Schmischke](https://tu-chemnitz.de/~miscmi) (michael.schmischke@math.tu-chemnitz.de) with contributions from Felix Bartel.
 
 If you want to contribute or have any questions, visit the [GitHub repository](https://github.com/NFFT/ANOVAapprox.jl/) to clone/fork the repository or open an issue.
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diff --git a/docs/src/index.md b/docs/src/index.md
index 257af08..abc927b 100644
--- a/docs/src/index.md
+++ b/docs/src/index.md
@@ -1,15 +1,25 @@
 # Welcome to ANOVAapprox.jl
 
-ANOVAapprox package
+This package provides a framework for the method ANOVAapprox to approximate high-dimensional functions with a low superposition dimension or a sparse ANOVA decomposition from scattered data. 
 
 ## Literature
 
 ```@raw html
 <ul>
-<li id="BaPoSc">[<a>Bartel, Potts, Schmischke, 2021</a>]
-  F. Bartel, D. Potts, M. Schmischke. Grouped transformations in high-dimensional explainable ANOVA approximation.</emph>
-  Preprint 2021.
-  arXiv: <a href="https://arxiv.org/abs/2010.10199">2010.10199</a>.
-</li>
+  <li>D. Potts und M. Schmischke <br> 
+  <b>Interpretable transformed ANOVA approximation on the example of the prevention of forest fires</b> <br>
+  <a href="https://arxiv.org/abs/2110.07353">arXiv</a>, <a href="https://www-user.tu-chemnitz.de/~potts/paper/anovatransf.pdf">PDF</a></li>
+  <li>D. Potts und M. Schmischke <br> 
+  <b>Interpretable approximation of high-dimensional data</b> <br>
+  SIAM Journal on Mathematics of Data Science (accepted) <br>
+  <a href="https://arxiv.org/abs/2103.13787">arXiv</a>, <a href="https://www-user.tu-chemnitz.de/~potts/paper/intanova.pdf">PDF</a>, <a href="https://github.com/NFFT/AttributeRankingExamples">Software</a></li>
+  <li>D. Potts und M. Schmischke <br> 
+  <b>Learning multivariate functions with low-dimensional structures using polynomial bases</b><br>
+  Journal of Computational and Applied Mathematics 403, 113821, 2021<br>
+  <a href="https://doi.org/10.1016/j.cam.2021.113821">DOI</a>, <a href="https://arxiv.org/abs/1912.03195">arXiv</a>, <a href="https://www-user.tu-chemnitz.de/~potts/paper/ANOVA_T.pdf">PDF</a></li>
+  <li>D. Potts und M. Schmischke <br> 
+  <b>Approximation of high-dimensional periodic functions with Fourier-based methods</b><br>
+  SIAM Journal on Numerical Analysis 59 (5), 2393-2429, 2021<br>
+  <a href="https://doi.org/10.1137/20M1354921">DOI</a>, <a href="https://arxiv.org/abs/1907.11412">arXiv</a>, <a href="https://www-user.tu-chemnitz.de/~potts/paper/anova.pdf">PDF</a></li>
 </ul>
 ```
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