#549 done

README.md

@@ -11,22 +11,50 @@ is a python toolkit, devoted to document level Attitude and Relation Extraction
 
 ## Description
 
-This toolkit aims to solve data preparation problems in Relation Extraction related taks, considiering such factors as:
+
+This toolkit aims at memory-effective data processing in Relation Extraction (RE) related tasks.
+
+<p align="center">
+ <img src="docs/arekit-pipeline-concept.png"/>
+</p>
+
+> Figure: AREkit pipelines design. More on 
+> **[ARElight: Context Sampling of Large Texts for Deep Learning Relation Extraction](https://www.ecir2024.org/accepted-paper/)** paper
+
+In particular, this framework serves the following features: 
+* ➿ [pipelines](https://github.com/nicolay-r/AREkit/wiki/Pipelines:-Text-Opinion-Annotation) and iterators for handling large-scale collections serialization without out-of-memory issues.
 * 🔗 EL (entity-linking) API support for objects, 
 * ➰ avoidance of cyclic connections,
 * :straight_ruler: distance consideration between relation participants (in `terms` or `sentences`),
 * 📑 relations annotations and filtering rules,
 * *️⃣ entities formatting or masking, and more.
-* ➿ [pipelines](https://github.com/nicolay-r/AREkit/wiki/Pipelines:-Text-Opinion-Annotation) and iterators for handling large-scale collections serialization without out-of-memory issues.
 
-The core functionality includes 
-(1) API for document presentation with EL (Entity Linking, i.e. Object Synonymy) support 
-for sentence level relations preparation (dubbed as contexts)
-(2) API for contexts extraction
-(3) relations transferring from sentence-level onto document-level, and more.
+The core functionality includes: 
+* API for document presentation with EL (Entity Linking, i.e. Object Synonymy) support 
+for sentence level relations preparation (dubbed as contexts);
+* API for contexts extraction;
+* Relations transferring from sentence-level onto document-level, and more.
 
 ## Installation 
 
 ```bash
 pip install git+https://github.com/nicolay-r/[email protected]
 ```
+
+## Usage
+
+Please follow the **[tutorial section on project Wiki](https://github.com/nicolay-r/AREkit/wiki/Tutorials)** for mode details.
+
+## How to cite
+A great research is also accompanied by the faithful reference. 
+if you use or extend our work, please cite as follows:
+
+```bibtex
+@inproceedings{rusnachenko2024arelight,
+ title={ARElight: Context Sampling of Large Texts for Deep Learning Relation Extraction},
+ author={Rusnachenko, Nicolay and Liang, Huizhi and Kolomeets, Maxim and Shi, Lei},
+ booktitle={European Conference on Information Retrieval},
+ year={2024},
+ organization={Springer}
+}
+```