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.
One example is below.
Q: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chinese Short Text Summarization Dataset (LCSTS) is constructed by BIBREF1 . The dataset consists of more than 2.4 million text-summary pairs in total, constructed from a famous Chinese social media microblogging service Weibo.
Question: Are results reported only for English data?
A: No
Rationale: Based on the context, the dataset is constructed from a famous Chinese social media microblogging service Weibo.
Q: To compare with our model, we select a number of strong summarization models as baseline systems. $ extsc {Lead-X}$ uses the top $X$ sentences as a summary BIBREF19. $ extsc {DRM}$ BIBREF10 leverages deep reinforcement learning for summarization. $ extsc {TConvS2S}$ BIBREF2 is based on convolutional neural networks. $ extsc {BottomUp}$ BIBREF11 uses a bottom-up approach to generate summarization. ABS BIBREF26 uses neural attention for summary generation. DRGD BIBREF27 is based on a deep recurrent generative decoder. To compare with our pretrain-only model, we include several unsupervised abstractive baselines: SEQ$^3$ BIBREF28 employs the reconstruction loss and topic loss for summarization. BottleSum BIBREF23 leverages unsupervised extractive and self-supervised abstractive methods. GPT-2 BIBREF7 is a large-scaled pretrained language model which can be directly used to generate summaries.
Question: What were the baselines?
A:
$ extsc {Lead-X}$ $ extsc {PTGen}$ $ extsc {DRM}$ $ extsc {TConvS2S}$ $ extsc {BottomUp}$ ABS DRGD SEQ$^3$ BottleSum GPT-2