Update README.md

README.md

@@ -141,7 +141,7 @@ CAMERA (CyberAgent Multimodal Evaluation for Ad Text GeneRAtion) is the Japanese
 
 - **Homepage:** [Github](https://github.com/CyberAgentAILab/camera)
 - **Paper:** [Striking Gold in Advertising: Standardization and Exploration of Ad Text
-Generation](https://arxiv.org/abs/2309.12030)
+Generation](https://aclanthology.org/2024.acl-long.54/)
   - [NEW!] Our paper has been accepted to [ACL2024](https://2024.aclweb.org/), and we will update the paper information as soon as the proceedings are published.
 
 ## Uses
@@ -264,12 +264,22 @@ DatasetDict({
 ## Citation
 
 ```
-@misc{mita2024striking,
-      title={Striking Gold in Advertising: Standardization and Exploration of Ad Text Generation},
-      author={Masato Mita and Soichiro Murakami and Akihiko Kato and Peinan Zhang},
-      year={2024},
-      eprint={2309.12030},
-      archivePrefix={arXiv},
-      primaryClass={id='cs.CL' full_name='Computation and Language' is_active=True alt_name='cmp-lg' in_archive='cs' is_general=False description='Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.'}
+@inproceedings{mita-etal-2024-striking,
+    title = "Striking Gold in Advertising: Standardization and Exploration of Ad Text Generation",
+    author = "Mita, Masato  and
+      Murakami, Soichiro  and
+      Kato, Akihiko  and
+      Zhang, Peinan",
+    editor = "Ku, Lun-Wei  and
+      Martins, Andre  and
+      Srikumar, Vivek",
+    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
+    month = aug,
+    year = "2024",
+    address = "Bangkok, Thailand and virtual meeting",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2024.acl-long.54",
+    pages = "955--972",
+    abstract = "In response to the limitations of manual ad creation, significant research has been conducted in the field of automatic ad text generation (ATG). However, the lack of comprehensive benchmarks and well-defined problem sets has made comparing different methods challenging. To tackle these challenges, we standardize the task of ATG and propose a first benchmark dataset, CAMERA, carefully designed and enabling the utilization of multi-modal information and facilitating industry-wise evaluations. Our extensive experiments with a variety of nine baselines, from classical methods to state-of-the-art models including large language models (LLMs), show the current state and the remaining challenges. We also explore how existing metrics in ATG and an LLM-based evaluator align with human evaluations.",
 }
 ```