Update README.md

README.md

@@ -9,7 +9,17 @@ datasets:
 metrics:
 - seqeval
 widget:
-- text: "Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a text. Since this is a time-consuming process, Artificial Intelligence is used to automate it. Currently, classical machine learning methods, that use statistics and linguistics, are widely used for the extraction process. The fact that these methods have been widely used in the community has the advantage that there are many easy-to-use libraries. Now with the recent innovations in NLP, transformers can be used to improve keyphrase extraction. Transformers also focus on the semantics and context of a document, which is quite an improvement."
+- text: "Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. 
+Thanks to these keyphrases humans can understand the content of a text very quickly and easily without reading 
+it completely. Keyphrase extraction was first done primarily by human annotators, who read the text in detail 
+and then wrote down the most important keyphrases. The disadvantage is that if you work with a lot of documents, 
+this process can take a lot of time. 
+
+Here is where Artificial Intelligence comes in. Currently, classical machine learning methods, that use statistical 
+and linguistic features, are widely used for the extraction process. Now with deep learning, it is possible to capture 
+the semantic meaning of a text even better than these classical methods. Classical methods look at the frequency, 
+occurrence and order of words in the text, whereas these neural approaches can capture long-term semantic dependencies 
+and context of words in a text."
   example_title: "Example 1"
 - text: "In this work, we explore how to learn task specific language models aimed towards learning rich representation of keyphrases from text documents. We experiment with different masking strategies for pre-training transformer language models (LMs) in discriminative as well as generative settings. In the discriminative setting, we introduce a new pre-training objective - Keyphrase Boundary Infilling with Replacement (KBIR), showing large gains in performance (up to 9.26 points in F1) over SOTA, when LM pre-trained using KBIR is fine-tuned for the task of keyphrase extraction. In the generative setting, we introduce a new pre-training setup for BART - KeyBART, that reproduces the keyphrases related to the input text in the CatSeq format, instead of the denoised original input. This also led to gains in performance (up to 4.33 points inF1@M) over SOTA for keyphrase generation. Additionally, we also fine-tune the pre-trained language models on named entity recognition(NER), question answering (QA), relation extraction (RE), abstractive summarization and achieve comparable performance with that of the SOTA, showing that learning rich representation of keyphrases is indeed beneficial for many other fundamental NLP tasks."
   example_title: "Example 2"
@@ -94,13 +104,21 @@ extractor = KeyphraseExtractionPipeline(model=model_name)
 ```python
 # Inference
 text = """
-Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a text. 
-Since this is a time-consuming process, Artificial Intelligence is used to automate it. 
-Currently, classical machine learning methods, that use statistics and linguistics, 
-are widely used for the extraction process. The fact that these methods have been widely used in the community 
-has the advantage that there are many easy-to-use libraries. Now with the recent innovations in NLP, 
-transformers can be used to improve keyphrase extraction. Transformers also focus on the semantics 
-and context of a document, which is quite an improvement.
+Keyphrase extraction is a technique in text analysis where you extract the 
+important keyphrases from a document. Thanks to these keyphrases humans can 
+understand the content of a text very quickly and easily without reading it 
+completely. Keyphrase extraction was first done primarily by human annotators, 
+who read the text in detail and then wrote down the most important keyphrases. 
+The disadvantage is that if you work with a lot of documents, this process 
+can take a lot of time. 
+
+Here is where Artificial Intelligence comes in. Currently, classical machine 
+learning methods, that use statistical and linguistic features, are widely used 
+for the extraction process. Now with deep learning, it is possible to capture 
+the semantic meaning of a text even better than these classical methods. 
+Classical methods look at the frequency, occurrence and order of words 
+in the text, whereas these neural approaches can capture long-term 
+semantic dependencies and context of words in a text.
 """
 
 keyphrases = extractor(text)
@@ -111,9 +129,8 @@ print(keyphrases)
 
 ```
 # Output
-['Artificial Intelligence' 'Keyphrase extraction' 'NLP'
- 'keyphrase extraction' 'linguistics' 'machine learning' 'semantics'
- 'statistics' 'text analysis']
+['Artificial Intelligence' 'Keyphrase extraction' 'deep learning'
+ 'features' 'text analysis']
 ```
 
 ## 📚 Training Dataset