Update README.md

README.md

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-Reliable Query-focused Summarization Tester (RQFT) is a dataset to evaluate Query-focused Summarization models. It contains 203 `<query, document, summary>` triples which can be used to evaluate Query-focused Summarizing models. Each document has more than $1$ query on an average ($1.41$ to be precise). This is a design choice to tackle Topic Centralization, see Baumel et al., 2016.
+---
+license: mit
+task_categories:
+- summarization
+language:
+- en
+tags:
+- chemistry
+- biology
+- art
+- music
+pretty_name: rqft
+size_categories:
+- n<1K
+---
+Reliable Query-focused Summarization Tester (RQFT) is a dataset to evaluate Query-focused Summarization models. It contains 203 `<query, document, summary>` triples which can be used to evaluate Query-focused Summarizing models. Each document has more than 1 query on an average (1.41 to be precise). This is a design choice to tackle Topic Centralization, see Baumel et al., 2016.
 For more details, we refer the reader to our EMNLP 2023 paper: [Reinforcement Replaces Supervision: Query focused Summarization using
 Deep Reinforcement Learning](https://aclanthology.org/2023.emnlp-main.977/)
 
+[Baumel et al., 2016] Tal Baumel, Raphael Cohen, and Michael Elhadad. 2016. Topic concentration in query focused summarization datasets. In AAAI Conference on Artificial Intelligence.
 
 Citation