Update README.md

README.md

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 - recall
 
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+<img src="https://public.3.basecamp.com/p/rs5XqmAuF1iEuW6U7nMHcZeY/upload/download/VL-NLP-short.png" alt="logo voicelab nlp" style="width:300px;"/>
+
 # Keyword Extraction from Short Texts with T5
 
-Our vlT5 model is a keyword generation model based on encoder-decoder architecture using Transformer blocks presented by Google ([https://huggingface.co/t5-base](https://huggingface.co/t5-base)). The vlT5 was trained on scientific articles corpus to predict a given set of keyphrases based on the concatenation of the article’s abstract and title. It generates precise, yet not always complete keyphrases that describe the content of the article based only on the abstract.
+ > Our vlT5 model is a keyword generation model based on encoder-decoder architecture using Transformer blocks presented by Google ([https://huggingface.co/t5-base](https://huggingface.co/t5-base)). The vlT5 was trained on scientific articles corpus to predict a given set of keyphrases based on the concatenation of the article’s abstract and title. It generates precise, yet not always complete keyphrases that describe the content of the article based only on the abstract.
+
+**Keywords generated with vlT5-base-keywords:** encoder-decoder architecture, keyword generation
+
+## vlT5
 
 The biggest advantage is the transferability of the vlT5 model, as it works well on all domains and types of text. The downside is that the text length and the number of keywords are similar to the training data: the text piece of an abstract length generates approximately 3 to 5 keywords. It works both extractive and abstractively. Longer pieces of text must be split into smaller chunks, and then propagated to the model.
 
@@ -36,7 +42,7 @@ The biggest advantage is the transferability of the vlT5 model, as it works well
 - **Language:** pl, en (but works relatively well with others)
 - **Training data:** POSMAC
 - **Online Demo:** [https://nlp-demo-1.voicelab.ai/](https://nlp-demo-1.voicelab.ai/)
-- **Paper:** [TBA](TBA)
+- **Paper:** [Keyword Extraction from Short Texts with a Text-To-Text Transfer Transformer, ACIIDS 2022](TBA)
 
 # Corpus