Summarizing NLP Research Papers

Readlist of NLP Research Paper Summary Blogs and Videos

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This blog is an outpost with the aim to organize all the blogs that I have written so far on Medium for explaining NLP research papers. Also for easy searchability, I have grouped all the blogs under a common high-level topic.

Before you start criticizing me for not giving credits to original authors in this post 😠 — All the information related to the author, organization, and paper is present in the respective blog posts. 😊 So Happy Reading……

P.S. I will keep updating this blog when I add more research paper summaries. (Last updated: 06/12/2021) — dd/mm/yyyy

Topics Covered so far…

1. On-Device NLP (1 paper)
2. Text Similarity (2 paper)
3. Text Summarization (5 papers)
4. Keyword Extraction (11 papers)
5. Query Expansion (2 papers)
6. Chatbot and Dialogue Systems (1 Paper)
7. Data Augmentation in NLP (3 paper)
8. Question Answering (1 paper)
9. Others (6 papers)

Total Papers = 32 ……. Many more to go …….

On-Device NLP refers to optimized, small-sized yet accurate NLP models that can be deployed on mobile devices directly. Below are some of the blogs that I have summarized so far —

1. Efficient System for Grammar Error Correction on Mobile Devices (Blog / Video)

Text similarity is the task of measuring the closeness value between any two text segments. They can be calculated at both the syntactic level (syntax oriented) as well as the semantic level (meaning oriented). Below are some of the blogs that I have summarized so far —

1. A Graph-based Text Similarity Method with Named Entity Information in NLP (Blog)
2. Aspect-based document similarity using Transformers (Blog, Video)

Text summarization is the task of shortening a set of document(s)?, to create a subset that represents the most important or relevant information within the original document(s)?. Below are some of the blogs that I have summarized so far—

1. PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization (Blog / Video)
2. Leveraging BERT for Extractive Text Summarization on Lectures (Blog / Video)
3. Understanding T5 Model: Text to Text Transfer Transformer Model (Blog / Video)
4. Entity-level Factual Consistency in Abstractive Text Summarization (Blog / Video)
5. Multi-Sentence Compression: Finding Shortest Paths in Word Graphs (Blog)

Keyword extraction is the task of identifying terms/phrases that best describe the content. They could be both extractive and abstractive. Below are some of the blogs that I have summarized so far —

1. 10 Popular Keyword Extraction Algorithms in Natural Language Processing (Blog)
2. EmbedRank: Simple Unsupervised Keyphrase Extraction using Sentence Embeddings (Blog / Video)

Query expansion is the process of reformulating a given query to improve the retrieval performance in Information Retrieval systems. Below are some of the blogs that I have summarized so far—

1. BERT-QE: Contextualized Query Expansion for Document Re-ranking (Blog / Video)
2. Neural Query Expansion for Code Search (Blog / Video)

A conversational agent is a dialogue system that understands and exchanges conversation with humans in natural language. Below are some of the blogs that I have summarized so far—

1. DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation (Blog / Video)

Data augmentation is a widely used technique to increase the amount of data to already existing data by adding slightly modified copies of it. Below are some of the blogs that I have summarized so far —

1. EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks (Blog / Video)
2. Data Augmentation using Pre-trained Transformer Model (BERT, GPT2, etc) (Video)
3. Text Data Augmentation Made Simple By Leveraging NLP Cloud APIs like spaCy, SyntaxNet, WordNet, NMT (Video)

Question answering (QA) is about building systems that automatically answer questions posed by humans in a natural language. Below are some of the blogs that I have summarized so far —

1. Training Question Answering Models from Synthetic Data (Blog / Video)

1. ByT5: Towards a token-free future with pre-trained byte-to-byte models (Blog)
2. Deep Natural Language Processing for LinkedIn Search Systems (Blog / Video)
3. Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing (Blog/Video)
4. Sentiment Lexicon-Based Features for Sentiment Analysis in Short Text (Blog)
5. Unsupervised Topic Segmentation of Meetings with BERT Embeddings (Video/Blog)
6. BERTScore: Evaluating Text Generation with BERT (Video/Blog)

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