In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

[Q]: Axelrod's causal mapping method comprises a set of conventions to graphically represent networks of causes and effects (the nodes in a network) as well as the qualitative aspects of this relation (the network’s directed edges, notably assertions of whether the causal linkage is positive or negative).
[A]: What are the causal mapping methods employed?


[Q]: This section reports the results of the experiments conducted on two data sets for evaluating the performances of wDTW and wTED against other baseline methods. We denote the following distance/similarity measures. WMD: The Word Mover's Distance introduced in Section SECREF1 . VSM: The similarity measure introduced in Section UID12 . PV-DTW: PV-DTW is the same as Algorithm SECREF21 except that the distance between two paragraphs is not based on Algorithm SECREF20 but rather computed as INLINEFORM0 where INLINEFORM1 is the PV embedding of paragraph INLINEFORM2 . PV-TED: PV-TED is the same as Algorithm SECREF23 except that the distance between two paragraphs is not based on Algorithm SECREF20 but rather computed as INLINEFORM0 .
[A]: Which are the state-of-the-art models?


[Q]: We show the final results for opinion recommendation, comparing our proposed model with the following state-of-the-art baseline systems:

RS-Average is the widely-adopted baseline (e.g., by Yelp.com), using the averaged review scores as the final score.

RS-Linear estimates the rating score that a user would give by INLINEFORM0 BIBREF49 , where INLINEFORM1 and INLINEFORM2 are the the training deviations of the user INLINEFORM3 and the product INLINEFORM4 , respectively.

RS-Item applies INLINEFORM0 NN to estimate the rating score BIBREF50 . We choose the cosine similarity between INLINEFORM1 to measure the distance between product.

RS-MF is a state-of-the-art recommendation model, which uses matrix factorisation to predict rating score BIBREF8 , BIBREF41 , BIBREF25 .

Sum-Opinosis uses a graph-based framework to generate abstractive summarisation given redundant opinions BIBREF51 .

Sum-LSTM-Att is a state-of-the-art neural abstractive summariser, which uses an attentional neural model to consolidate information from multiple text sources, generating summaries using LSTM decoding BIBREF44 , BIBREF3 .

All the baseline models are single-task models, without considering rating and summarisation prediction jointly. The results are shown in Table TABREF46 . 
[A]:
What are the baselines?