In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

Q: Modern Standard Arabic (MSA) and Classical Arabic (CA) have two types of vowels, namely long vowels, which are explicitly written, and short vowels, aka diacritics, which are typically omitted in writing but are reintroduced by readers to properly pronounce words. We show that our model achieves a case ending error rate (CEER) of 3.7% for MSA and 2.5% for CA. For MSA, this CEER is more than 60% lower than other state-of-the-art systems such as Farasa and the RDI diacritizer, which are trained on the same dataset and achieve CEERs of 10.7% and 14.4% respectively.  
 Question: what are the previous state of the art?

A: Farasa RDI
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Q: We compare our model with the following state-of-the-art joint entity and relation extraction models:

(1) SPTree BIBREF4: This is an end-to-end neural entity and relation extraction model using sequence LSTM and Tree LSTM. (2) Tagging BIBREF5: This is a neural sequence tagging model which jointly extracts the entities and relations using an LSTM encoder and an LSTM decoder. (3) CopyR BIBREF6: This model uses an encoder-decoder approach for joint extraction of entities and relations. (4) HRL BIBREF11: This model uses a reinforcement learning (RL) algorithm with two levels of hierarchy for tuple extraction. (5) GraphR BIBREF14: This model considers each token in a sentence as a node in a graph, and edges connecting the nodes as relations between them. (6) N-gram Attention BIBREF9: This model uses an encoder-decoder approach with N-gram attention mechanism for knowledge-base completion using distantly supervised data. 
 Question: What is previous work authors reffer to?

A: SPTree Tagging CopyR HRL GraphR N-gram Attention
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Q: We compared several summarization methods which can be categorized into three groups: unsupervised, non-neural supervised, and neural supervised methods. For the unsupervised methods, we tested: SumBasic, which uses word frequency to rank sentences and selects top sentences as the summary BIBREF13 , BIBREF14 . Lsa, which uses latent semantic analysis (LSA) to decompose the term-by-sentence matrix of a document and extracts sentences based on the result. We experimented with the two approaches proposed in BIBREF15 and BIBREF16 respectively. LexRank, which constructs a graph representation of a document, where nodes are sentences and edges represent similarity between two sentences, and runs PageRank algorithm on that graph and extracts sentences based on the resulting PageRank values BIBREF17 . TextRank, which is very similar to LexRank but computes sentence similarity based on the number of common tokens BIBREF19 . Bayes, which represents each sentence as a feature vector and uses naive Bayes to classify them BIBREF5 . The original paper computes TF-IDF score on multi-word tokens that are identified automatically using mutual information. We did not do this identification, so our TF-IDF computation operates on word tokens. Hmm, which uses hidden Markov model where states correspond to whether the sentence should be extracted BIBREF20 . The original work uses QR decomposition for sentence selection but our implementation does not. We simply ranked the sentences by their scores and picked the top 3 as the summary. MaxEnt, which represents each sentence as a feature vector and leverages maximum entropy model to compute the probability of a sentence should be extracted BIBREF21 . The original approach puts a prior distribution over the labels but we put the prior on the weights instead. Our implementation still agrees with the original because we employed a bias feature which should be able to learn the prior label distribution. As for the neural supervised method, we evaluated NeuralSum BIBREF11 using the original implementation by the authors. We modified their implementation slightly to allow for evaluating the model with ROUGE. Note that all the methods are extractive. Our implementation code for all the methods above is available online. As a baseline, we used Lead-N which selects INLINEFORM0 leading sentences as the summary. For all methods, we extracted 3 sentences as the summary since it is the median number of sentences in the gold summaries that we found in our exploratory analysis. 
 Question: Which approaches did they use?

A:
SumBasic Lsa LexRank TextRank Bayes Hmm MaxEnt NeuralSum Lead-N
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