Update README.md

README.md

@@ -119,9 +119,23 @@ SentenceTransformer(
 If you use this work, please cite:
 
 ```bibtex
-@inproceedings{MeloSemantic,
-	author = {Melo, Rui and Santos, Professor Pedro Alexandre and Dias, Professor Jo{\~ a}o},
-	title = {A {Semantic} {Search} {System} for {Supremo} {Tribunal} de {Justi}{\c c}a},
+@InProceedings{MeloSemantic,
+  author="Melo, Rui
+  and Santos, Pedro A.
+  and Dias, Jo{\~a}o",
+  editor="Moniz, Nuno
+  and Vale, Zita
+  and Cascalho, Jos{\'e}
+  and Silva, Catarina
+  and Sebasti{\~a}o, Raquel",
+  title="A Semantic Search System for the Supremo Tribunal de Justi{\c{c}}a",
+  booktitle="Progress in Artificial Intelligence",
+  year="2023",
+  publisher="Springer Nature Switzerland",
+  address="Cham",
+  pages="142--154",
+  abstract="Many information retrieval systems use lexical approaches to retrieve information. Such approaches have multiple limitations, and these constraints are exacerbated when tied to specific domains, such as the legal one. Large language models, such as BERT, deeply understand a language and may overcome the limitations of older methodologies, such as BM25. This work investigated and developed a prototype of a Semantic Search System to assist the Supremo Tribunal de Justi{\c{c}}a (Portuguese Supreme Court of Justice) in its decision-making process. We built a Semantic Search System that uses specially trained BERT models (Legal-BERTimbau variants) and a Hybrid Search System that incorporates both lexical and semantic techniques by combining the capabilities of BM25 and the potential of Legal-BERTimbau. In this context, we obtained a {\$}{\$}335{\backslash}{\%}{\$}{\$}335{\%}increase on the discovery metric when compared to BM25 for the first query result. This work also provides information on the most relevant techniques for training a Large Language Model adapted to Portuguese jurisprudence and introduces a new technique of Metadata Knowledge Distillation.",
+  isbn="978-3-031-49011-8"
 }
 
 @inproceedings{souza2020bertimbau,