Pierre Achkar

Pierre Achkar, Tim Gollub, Arno Simons et al.

Existing benchmarks for systematic reviewing remain limited either in scale or in disciplinary coverage, with some collections comprising only a modest number of topics and others focusing primarily on biomedical research. We present Webis-SR4ALL-26, a large-scale, cross-disciplinary corpus of 301,871 systematic reviews spanning all scientific fields as covered by OpenAlex. Using a multi-stage pre-processing pipeline, we link reviews to resolved OpenAlex metadata and reference lists and extract, when explicitly reported, structured method artifacts relevant to retrieval and screening. These artifacts include reported search strategies (Boolean queries or keyword lists) that we normalize into executable approximations, as well as reported inclusion and exclusion criteria. Together, these layers support cross-domain benchmarking of retrieval and screening components against review reference lists, training and evaluation of extraction methods for review artifacts, and comparative meta-science analyses of systematic review practices across disciplines and time. To demonstrate one concrete use case, we report large-scale baseline retrieval signals by executing normalized search strategies in OpenAlex and comparing retrieved sets to resolved reference lists. We release the corpus and the pre-processing pipeline, along with code used for extraction validation and the retrieval demonstration.

Pierre Achkar, Tim Gollub amd Martin Potthast

Systematic reviews are the standard method for synthesizing scientific evidence, but their creation requires substantial manual effort, particularly during retrieval and screening. While recent work has explored automating these steps, evaluation resources remain largely confined to the biomedical domain, limiting reproducible experimentation in other domains. This paper introduces SR4CS, a large-scale collection of systematic reviews in computer science, designed to support reproducible research on Boolean query generation, retrieval, and screening. The corpus comprises 1,212 systematic reviews with their original expert-designed Boolean search queries, 104,316 resolved references, and structured methodological metadata. For controlled evaluation, the original Boolean queries are additionally provided in a normalized, approximated form operating over titles and abstracts. To illustrate the intended use of the collection, baseline experiments compare the approximated expert Boolean queries with zero-shot LLM-generated Boolean queries, BM25, and dense retrieval under a unified evaluation setting. The results highlight systematic differences in precision, recall, and ranking behavior across retrieval paradigms and expose limitations of naive zero-shot Boolean generation. SR4CS is released under an open license on Zenodo (https://doi.org/10.5281/zenodo.17163932), together with documentation and code (https://github.com/webis-de/scolia26-sr4cs), to enable reproducible evaluation and future research on scaling systematic review automation.

Pierre Achkar, Tim Gollub, Martin Potthast

The exponential growth of scientific publications has made it increasingly difficult for researchers to stay updated and synthesize knowledge effectively. This paper presents XSum, a modular pipeline for multi-document summarization (MDS) in the scientific domain using Retrieval-Augmented Generation (RAG). The pipeline includes two core components: a question-generation module and an editor module. The question-generation module dynamically generates questions adapted to the input papers, ensuring the retrieval of relevant and accurate information. The editor module synthesizes the retrieved content into coherent and well-structured summaries that adhere to academic standards for proper citation. Evaluated on the SurveySum dataset, XSum demonstrates strong performance, achieving considerable improvements in metrics such as CheckEval, G-Eval and Ref-F1 compared to existing approaches. This work provides a transparent, adaptable framework for scientific summarization with potential applications in a wide range of domains. Code available at https://github.com/webis-de/scolia25-xsum