UNIST-Eunchan/NLP-Paper-to-QA-Generation huggingface dataset:
Abstract: Unsupervised representation learning for tweets is an important research field which helps in solving several business applications such as sentiment analysis, hashtag prediction, paraphrase detection and microblog ranking. A good tweet representation learning model must handle the idiosyncratic nature of tweets which poses several challenges such as short length, informal words, unusual grammar and misspellings. However, there is a lack of prior work which surveys the representation learning models with a focus on tweets. In this work, we organize the models based on its objective function which aids the understanding of the literature. We also provide interesting future directions, which we believe are fruitful in advancing this field by building high-quality tweet representation learning models.

Introduction: Twitter is a widely used microblogging platform, where users post and interact with messages, “tweets”. Understanding the semantic representation of tweets can benefit a plethora of applications such as sentiment analysis BIBREF0 , BIBREF1 , hashtag prediction BIBREF2 , paraphrase detection BIBREF3 and microblog ranking BIBREF4 , BIBREF5 . However, tweets are difficult to model as they pose several challenges such as short length, informal words, unusual grammar and misspellings. Recently, researchers are focusing on leveraging unsupervised representation learning methods based on neural networks to solve this problem. Once these representations are learned, we can use off-the-shelf predictors taking the representation as input to solve the downstream task BIBREF6 , BIBREF7 . These methods enjoy several advantages: (1) they are cheaper to train, as they work with unlabelled data, (2) they reduce the dependence on domain level experts, and (3) they are highly effective across multiple applications, in practice. Despite this, there is a lack of prior work which surveys the tweet-specific unsupervised representation learning models. In this work, we attempt to fill this gap by investigating the models in an organized fashion. Specifically, we group the models based on the objective function it optimizes. We believe this work can aid the understanding of the existing literature. We conclude the paper by presenting interesting future research directions, which we believe are fruitful in advancing this field by building high-quality tweet representation learning models.

Question: How do they encourage understanding of literature as part of their objective function?
Answer: They group the existing works in terms of the objective function they optimize - within-tweet relationships, inter-tweet relationships, autoencoder, and weak supervision.
Abstract: The SemEval-2010 benchmark dataset has brought renewed attention to the task of automatic keyphrase extraction. This dataset is made up of scientific articles that were automatically converted from PDF format to plain text and thus require careful preprocessing so that irrevelant spans of text do not negatively affect keyphrase extraction performance. In previous work, a wide range of document preprocessing techniques were described but their impact on the overall performance of keyphrase extraction models is still unexplored. Here, we re-assess the performance of several keyphrase extraction models and measure their robustness against increasingly sophisticated levels of document preprocessing.

Introduction:  This work is licensed under a Creative Commons Attribution 4.0 International Licence. Licence details: http://creativecommons.org/licenses/by/4.0/ Recent years have seen a surge of interest in automatic keyphrase extraction, thanks to the availability of the SemEval-2010 benchmark dataset BIBREF0 . This dataset is composed of documents (scientific articles) that were automatically converted from PDF format to plain text. As a result, most documents contain irrelevant pieces of text (e.g. muddled sentences, tables, equations, footnotes) that require special handling, so as to not hinder the performance of keyphrase extraction systems. In previous work, these are usually removed at the preprocessing step, but using a variety of techniques ranging from simple heuristics BIBREF1 , BIBREF2 , BIBREF3 to sophisticated document logical structure detection on richly-formatted documents recovered from Google Scholar BIBREF4 . Under such conditions, it may prove difficult to draw firm conclusions about which keyphrase extraction model performs best, as the impact of preprocessing on overall performance cannot be properly quantified. While previous work clearly states that efficient document preprocessing is a prerequisite for the extraction of high quality keyphrases, there is, to our best knowledge, no empirical evidence of how preprocessing affects keyphrase extraction performance. In this paper, we re-assess the performance of several state-of-the-art keyphrase extraction models at increasingly sophisticated levels of preprocessing. Three incremental levels of document preprocessing are experimented with: raw text, text cleaning through document logical structure detection, and removal of keyphrase sparse sections of the document. In doing so, we present the first consistent comparison of different keyphrase extraction models and study their robustness over noisy text. More precisely, our contributions are:

Question: what keyphrase extraction models were reassessed?
Answer: Answer with content missing: (LVL1, LVL2, LVL3) 
- Stanford CoreNLP
- Optical Character Recognition (OCR) system, ParsCIT 
- further abridge the input text from level 2 preprocessed documents to the following: title, headers, abstract, introduction, related work, background and conclusion.
Abstract: The paper describes experiments on estimating emotion intensity in tweets using a generalized regressor system. The system combines lexical, syntactic and pre-trained word embedding features, trains them on general regressors and finally combines the best performing models to create an ensemble. The proposed system stood 3rd out of 22 systems in the leaderboard of WASSA-2017 Shared Task on Emotion Intensity.

Introduction: Twitter, a micro-blogging and social networking site has emerged as a platform where people express themselves and react to events in real-time. It is estimated that nearly 500 million tweets are sent per day . Twitter data is particularly interesting because of its peculiar nature where people convey messages in short sentences using hashtags, emoticons, emojis etc. In addition, each tweet has meta data like location and language used by the sender. It's challenging to analyze this data because the tweets might not be grammatically correct and the users tend to use informal and slang words all the time. Hence, this poses an interesting problem for NLP researchers. Any advances in using this abundant and diverse data can help understand and analyze information about a person, an event, a product, an organization or a country as a whole. Many notable use cases of the twitter can be found here. Along the similar lines, The Task 1 of WASSA-2017 BIBREF0 poses a problem of finding emotion intensity of four emotions namely anger, fear, joy, sadness from tweets. In this paper, we describe our approach and experiments to solve this problem. The rest of the paper is laid out as follows: Section 2 describes the system architecture, Section 3 reports results and inference from different experiments, while Section 4 points to ways that the problem can be further explored.

Question: what pretrained word embeddings were used?
Answer: Pretrained word embeddings  were not used
Abstract: This paper investigates two different neural architectures for the task of relation classification: convolutional neural networks and recurrent neural networks. For both models, we demonstrate the effect of different architectural choices. We present a new context representation for convolutional neural networks for relation classification (extended middle context). Furthermore, we propose connectionist bi-directional recurrent neural networks and introduce ranking loss for their optimization. Finally, we show that combining convolutional and recurrent neural networks using a simple voting scheme is accurate enough to improve results. Our neural models achieve state-of-the-art results on the SemEval 2010 relation classification task.

Introduction: Relation classification is the task of assigning sentences with two marked entities to a predefined set of relations. The sentence “We poured the <e1>milk</e1> into the <e2>pumpkin mixture</e2>.”, for example, expresses the relation Entity-Destination(e1,e2). While early research mostly focused on support vector machines or maximum entropy classifiers BIBREF0 , BIBREF1 , recent research showed performance improvements by applying neural networks (NNs) BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 on the benchmark data from SemEval 2010 shared task 8 BIBREF8 . This study investigates two different types of NNs: recurrent neural networks (RNNs) and convolutional neural networks (CNNs) as well as their combination. We make the following contributions: (1) We propose extended middle context, a new context representation for CNNs for relation classification. The extended middle context uses all parts of the sentence (the relation arguments, left of the relation arguments, between the arguments, right of the arguments) and pays special attention to the middle part. (2) We present connectionist bi-directional RNN models which are especially suited for sentence classification tasks since they combine all intermediate hidden layers for their final decision. Furthermore, the ranking loss function is introduced for the RNN model optimization which has not been investigated in the literature for relation classification before. (3) Finally, we combine CNNs and RNNs using a simple voting scheme and achieve new state-of-the-art results on the SemEval 2010 benchmark dataset.

Question: By how much does their best model outperform the state-of-the-art?
Answer: Best proposed model achieves F1 score of 84.9 compared to best previous result of 84.1.
Abstract: Segmenting an unordered text document into different sections is a very useful task in many text processing applications like multiple document summarization, question answering, etc. This paper proposes structuring of an unordered text document based on the keywords in the document. We test our approach on Wikipedia documents using both statistical and predictive methods such as the TextRank algorithm and Google's USE (Universal Sentence Encoder). From our experimental results, we show that the proposed model can effectively structure an unordered document into sections.

Introduction: To structure an unordered document is an essential task in many applications. It is a post-requisite for applications like multiple document extractive text summarization where we have to present a summary of multiple documents. It is a prerequisite for applications like question answering from multiple documents where we have to present an answer by processing multiple documents. In this paper, we address the task of segmenting an unordered text document into different sections. The input document/summary that may have unordered sentences is processed so that it will have sentences clustered together. Clustering is based on the similarity with the respective keyword as well as with the sentences belonging to the same cluster. Keywords are identified and clusters are formed for each keyword. We use TextRank algorithm BIBREF0 to extract the keywords from a text document. TextRank is a graph-based ranking algorithm which decides the importance of a vertex within a graph by considering the global information recursively computed from the entire graph rather than focusing on local vertex specific information. The model uses knowledge acquired from the entire text to extract the keywords. A cluster is generated for every keyword. While generating clusters, the similarity between a sentence and a topic/keyword is calculated using the cosine similarity of embeddings generated using Google's USE (Universal Sentence Encoder) BIBREF1 . USE is claimed to have better performance of transfer learning when used with sentence-level embeddings as compared to word-level embeddings alone. This model is claimed to have better performance even if there is less task-specific training data available. We observed that the quality of clusters/section is better if the similarity of a sentence with the keyword is considered along with the similarity of the sentence with the sentences already available in the respective section. To test our approach, we jumble the ordering of sentences in a document, process the unordered document and compare the similarity of the output document with the original document.

Question: What is an unordered text document, do these arise in real-world corpora?
Answer: A unordered text document is one where sentences in the document are disordered or jumbled. It doesn't appear that unordered text documents appear in corpora, but rather are introduced as part of processing pipeline.
Abstract: Biomedical Information Extraction is an exciting field at the crossroads of Natural Language Processing, Biology and Medicine. It encompasses a variety of different tasks that require application of state-of-the-art NLP techniques, such as NER and Relation Extraction. This paper provides an overview of the problems in the field and discusses some of the techniques used for solving them.

Introduction: The explosion of available scientific articles in the Biomedical domain has led to the rise of Biomedical Information Extraction (BioIE). BioIE systems aim to extract information from a wide spectrum of articles including medical literature, biological literature, electronic health records, etc. that can be used by clinicians and researchers in the field. Often the outputs of BioIE systems are used to assist in the creation of databases, or to suggest new paths for research. For example, a ranked list of interacting proteins that are extracted from biomedical literature, but are not present in existing databases, can allow researchers to make informed decisions about which protein/gene to study further. Interactions between drugs are necessary for clinicians who simultaneously administer multiple drugs to their patients. A database of diseases, treatments and tests is beneficial for doctors consulting in complicated medical cases. The main problems in BioIE are similar to those in Information Extraction: This paper discusses, in each section, various methods that have been adopted to solve the listed problems. Each section also highlights the difficulty of Information Extraction tasks in the biomedical domain. This paper is intended as a primer to Biomedical Information Extraction for current NLP researchers. It aims to highlight the diversity of the various techniques from Information Extraction that have been applied in the Biomedical domain. The state of biomedical text mining is reviewed regularly. For more extensive surveys, consult BIBREF0 , BIBREF1 , BIBREF2 .

Question: What is NER?
Answer: Named Entity Recognition, including entities such as proteins, genes, diseases, treatments, drugs, etc. in the biomedical domain
Abstract: The present study describes our submission to SemEval 2018 Task 1: Affect in Tweets. Our Spanish-only approach aimed to demonstrate that it is beneficial to automatically generate additional training data by (i) translating training data from other languages and (ii) applying a semi-supervised learning method. We find strong support for both approaches, with those models outperforming our regular models in all subtasks. However, creating a stepwise ensemble of different models as opposed to simply averaging did not result in an increase in performance. We placed second (EI-Reg), second (EI-Oc), fourth (V-Reg) and fifth (V-Oc) in the four Spanish subtasks we participated in.

Introduction: Understanding the emotions expressed in a text or message is of high relevance nowadays. Companies are interested in this to get an understanding of the sentiment of their current customers regarding their products and the sentiment of their potential customers to attract new ones. Moreover, changes in a product or a company may also affect the sentiment of a customer. However, the intensity of an emotion is crucial in determining the urgency and importance of that sentiment. If someone is only slightly happy about a product, is a customer willing to buy it again? Conversely, if someone is very angry about customer service, his or her complaint might be given priority over somewhat milder complaints.  BIBREF0 present four tasks in which systems have to automatically determine the intensity of emotions (EI) or the intensity of the sentiment (Valence) of tweets in the languages English, Arabic, and Spanish. The goal is to either predict a continuous regression (reg) value or to do ordinal classification (oc) based on a number of predefined categories. The EI tasks have separate training sets for four different emotions: anger, fear, joy and sadness. Due to the large number of subtasks and the fact that this language does not have many resources readily available, we only focus on the Spanish subtasks. Our work makes the following contributions: Our submissions ranked second (EI-Reg), second (EI-Oc), fourth (V-Reg) and fifth (V-Oc), demonstrating that the proposed method is accurate in automatically determining the intensity of emotions and sentiment of Spanish tweets. This paper will first focus on the datasets, the data generation procedure, and the techniques and tools used. Then we present the results in detail, after which we perform a small error analysis on the largest mistakes our model made. We conclude with some possible ideas for future work.

Question: What dataset did they use?
Answer:  Selection of tweets with for each tweet a label describing the intensity of the emotion or sentiment provided by organizers and  tweets translated form English to Spanish.
Abstract: Our entry into the HAHA 2019 Challenge placed $3^{rd}$ in the classification task and $2^{nd}$ in the regression task. We describe our system and innovations, as well as comparing our results to a Naive Bayes baseline. A large Twitter based corpus allowed us to train a language model from scratch focused on Spanish and transfer that knowledge to our competition model. To overcome the inherent errors in some labels we reduce our class confidence with label smoothing in the loss function. All the code for our project is included in a GitHub repository for easy reference and to enable replication by others.

Introduction: - !`Socorro, me ha picado una víbora! - ?`Cobra? - No, gratis.[5] Google Translation: - Help, I was bitten by a snake! - Does it charge? - Not free. [4]https://github.com/bfarzin/haha_2019_final, Accessed on 19 June 2019 [5]https://www.fluentin3months.com/spanish-jokes/, Accessed on 19 June 2019 Humor does not translate well because it often relies on double-meaning or a subtle play on word choice, pronunciation, or context. These issues are further exacerbated in areas where space is a premium (as frequent on social media platforms), often leading to usage and development of shorthand, in-jokes, and self-reference. Thus, building a system to classify the humor of tweets is a difficult task. However, with transfer-learning and the Fast.ai library, we can build a high quality classifier in a foreign language. Our system outperforms a Naive Bayes Support Vector Machine (NBSVM) baseline, which is frequently considered a "strong baseline" for many Natural Language Processing (NLP) related tasks (see Wang et al BIBREF0 ). Rather than hand-crafted language features, we have taken an "end to end" approach building from the raw text to a final model that achieves the tasks as presented. Our paper lays out the details of the system and our code can be found in a GitHub repository for use by other researchers to extend the state of the art in sentiment analysis.

Question: What were their results on the classification and regression tasks
Answer: F1 score result of 0.8099
Abstract: This article presents a preliminary approach towards characterizing political fake news on Twitter through the analysis of their meta-data. In particular, we focus on more than 1.5M tweets collected on the day of the election of Donald Trump as 45th president of the United States of America. We use the meta-data embedded within those tweets in order to look for differences between tweets containing fake news and tweets not containing them. Specifically, we perform our analysis only on tweets that went viral, by studying proxies for users' exposure to the tweets, by characterizing accounts spreading fake news, and by looking at their polarization. We found significant differences on the distribution of followers, the number of URLs on tweets, and the verification of the users.

Introduction: 10pt 1.10pt [ Characterizing Political Fake News in Twitter by its Meta-DataJulio Amador Díaz LópezAxel Oehmichen Miguel Molina-Solana( j.amador, axelfrancois.oehmichen11, mmolinas@imperial.ac.uk ) Imperial College London This article presents a preliminary approach towards characterizing political fake news on Twitter through the analysis of their meta-data. In particular, we focus on more than 1.5M tweets collected on the day of the election of Donald Trump as 45th president of the United States of America. We use the meta-data embedded within those tweets in order to look for differences between tweets containing fake news and tweets not containing them. Specifically, we perform our analysis only on tweets that went viral, by studying proxies for users' exposure to the tweets, by characterizing accounts spreading fake news, and by looking at their polarization. We found significant differences on the distribution of followers, the number of URLs on tweets, and the verification of the users. ]

Question: What are the characteristics of the accounts that spread fake news?
Answer: Accounts that spread fake news are mostly unverified, recently created and have on average high friends/followers ratio