Merge code for conf27/inductive-nlg-for-shared-tasks/

.gitignore

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conf27/inductive-nlg-for-shared-tasks/README.md

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+# Inductive Natural Language Generation for Shared Tasks
+
+## Overview
+
+This project investigates how AI agents can support shared tasks as human-driven experiments. The goal is not to automate shared tasks, but to study how agents can help researchers understand and communicate a solution space by synthesizing example solutions into an accessible synopsis.
+
+## Proposed Challenge
+
+The primary task is to generate a shared-task overview from participating systems' notebook papers, submissions, and—where available—prompt or communication logs. A complementary task may generate notebook papers directly from submissions, providing practical value while avoiding train–test leakage.
+
+Expected corpus structure:
+
+```text
+task/
+├── papers/
+│   ├── paper-1.pdf
+│   └── paper-n.pdf
+├── overview.pdf
+└── summaries.json
+```
+
+Systems will be evaluated through qualitative expert review and comparative automated judging. The evaluation must account for overview papers that describe teams without corresponding notebook papers.
+
+## Data Sources
+
+Candidate collections include shared tasks from [CLEF](https://clef-initiative.eu/), TREC, NTCIR, FIRE, SemEval, PAN, and Touché. Existing resources include the [TIRA shared-task collection](https://git.webis.de/code-research/tira/tira-shared-tasks) and canonical CLEF overview/notebook-paper proceedings.
+
+## Resources
+
+- [Proposal draft](https://www.overleaf.com/7375677649nhbknmygttkd#56811b)
+- [UniAgent code and baselines](https://github.com/uniagent-webis-de/uniagent-code)
+- [Archived corpus repository](https://github.com/uniagent-webis-de/uniagent_inlg_26-ARCHIVED)

conf27/inductive-nlg-for-shared-tasks/baseline-naive/Dockerfile

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+FROM python:3-slim
+
+ADD requirements.txt /
+
+RUN pip3 install --no-cache-dir -r /requirements.txt \
+	&& pip3 --no-cache-dir install pytest \
+	&& rm /requirements.txt
+
+ADD *.py /
+
+RUN cd / \
+	&& PYTHONPATH=/ pytest test_baseline.py
+

conf27/inductive-nlg-for-shared-tasks/baseline-naive/README.md

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+# Naive Baseline
+
+This deterministic baseline extracts each `paper.txt.md` title and abstract.
+
+```bash
+python3 baseline.py \
+  --input ../corpora-in-progress/touche-20-task-1-spot-check/papers \
+  --output predictions.jsonl \
+  --summary title-and-abstract
+```
+
+`--summary` accepts `title`, `abstract`, or `title-and-abstract`. The output
+contains one JSON object per line:
+
+```json
+{"id": "172", "summary": "Argument Retrieval Using Deep Neural Ranking Models"}
+```