Update README.md

README.md

@@ -1,7 +1,7 @@
 ## Dataset Summary
 
 A dataset for benchmarking keyphrase extraction and generation techniques from abstracts of english scientific papers. For more details about the dataset please refer the original paper - [https://dl.acm.org/doi/pdf/10.3115/1119355.1119383](https://dl.acm.org/doi/pdf/10.3115/1119355.1119383)
-Original source of the data - []()
+Data source - [https://github.com/boudinfl/ake-datasets/tree/master/datasets/Inspec](https://github.com/boudinfl/ake-datasets/tree/master/datasets/Inspec)
 
 
 ## Dataset Structure
@@ -165,6 +165,8 @@ print("Abstractive/absent Keyphrases: ", test_sample["abstractive_keyphrases"])
 print("\n-----------\n")
 ```
 ## Citation Information
+Please cite the works below if you use this dataset in your work.
+
 ```
 @inproceedings{hulth2003improved,
   title={Improved automatic keyword extraction given more linguistic knowledge},
@@ -174,6 +176,37 @@ print("\n-----------\n")
   year={2003}
 }
 ```
+and
+
+```
+@InProceedings{10.1007/978-3-030-45442-5_41,
+author="Sahrawat, Dhruva
+and Mahata, Debanjan
+and Zhang, Haimin
+and Kulkarni, Mayank
+and Sharma, Agniv
+and Gosangi, Rakesh
+and Stent, Amanda
+and Kumar, Yaman
+and Shah, Rajiv Ratn
+and Zimmermann, Roger",
+editor="Jose, Joemon M.
+and Yilmaz, Emine
+and Magalh{\~a}es, Jo{\~a}o
+and Castells, Pablo
+and Ferro, Nicola
+and Silva, M{\'a}rio J.
+and Martins, Fl{\'a}vio",
+title="Keyphrase Extraction as Sequence Labeling Using Contextualized Embeddings",
+booktitle="Advances in Information Retrieval",
+year="2020",
+publisher="Springer International Publishing",
+address="Cham",
+pages="328--335",
+abstract="In this paper, we formulate keyphrase extraction from scholarly articles as a sequence labeling task solved using a BiLSTM-CRF, where the words in the input text are represented using deep contextualized embeddings. We evaluate the proposed architecture using both contextualized and fixed word embedding models on three different benchmark datasets, and compare with existing popular unsupervised and supervised techniques. Our results quantify the benefits of: (a) using contextualized embeddings over fixed word embeddings; (b) using a BiLSTM-CRF architecture with contextualized word embeddings over fine-tuning the contextualized embedding model directly; and (c) using domain-specific contextualized embeddings (SciBERT). Through error analysis, we also provide some insights into why particular models work better than the others. Lastly, we present a case study where we analyze different self-attention layers of the two best models (BERT and SciBERT) to better understand their predictions.",
+isbn="978-3-030-45442-5"
+}
+```
 
 ## Contributions
 Thanks to [@debanjanbhucs](https://github.com/debanjanbhucs), [@dibyaaaaax](https://github.com/dibyaaaaax) and [@ad6398](https://github.com/ad6398) for adding this dataset