Challenge: Existing methods that use context for sentence classification are difficult to scale . Usually, sentences are treated as separate instances for the task . however, in many situations the sentence that is the focus of classification appears in a context that can provide additional information.
Approach: They propose a method that uses potentially large contexts to classify sentences . they use an LSTM, and short-span features to classize sentences based on a stacked CNN .
Outcome: The proposed method consistently improves on two different datasets.

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Challenge: Existing models for sentence classification use linear convolution, which may not be sufficient to model the non-consecutive dependency of the phrase and may overfit the sequential information.
Approach: They propose a model that extracts multi-scale n-gram features for understanding the semantic meaning of sentences by some key-phrases located at different positions.
Outcome: The proposed model outperforms existing models on eight benchmark datasets and is competitive against state-of-the-art models.
Hierarchical Neural Networks for Sequential Sentence Classification in Medical Scientific Abstracts (D18-1)

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Challenge: Existing sentences classification models often classify sentences in isolation without considering the context in which sentences appear.
Approach: They propose a hierarchical sequential labeling network to make use of contextual information within surrounding sentences to help classify the current sentence.
Outcome: The proposed model outperforms the state-of-the-art methods by 2%-3% on two benchmarking datasets for sequential sentence classification in medical scientific abstracts.
Modularized Syntactic Neural Networks for Sentence Classification (2020.emnlp-main)

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Challenge: Existing models for sentence classification use local information of sub-trees, but new models use global context .
Approach: They propose a tree-parallel mini-batch strategy for efficient training and predicting sentences . they propose to use syntax category labels to model sub-trees .
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Relating Simple Sentence Representations in Deep Neural Networks and the Brain (P19-1)

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Challenge: Existing deep learning models for natural language processing are not fully studied.
Approach: They investigate whether deep recurrent models learn sentences against those encoded by the brain and whether there is any correspondence between hidden layers of these models and brain regions when processing sentences.
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Deep Attentive Sentence Ordering Network (D18-1)

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Challenge: Existing methods for sentence ordering tasks rely on linguistic knowledge and are domain specific.
Approach: They propose a deep attentive sentence ordering network which integrates self-attention mechanism with LSTMs in the encoding of input sentences.
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A Span-based Dynamic Local Attention Model for Sequential Sentence Classification (2021.acl-short)

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Challenge: Existing methods for sentence classification ignore latent segment structure of document, in which contiguous sentences have coherent semantics.
Approach: They propose a span-based dynamic local attention model that captures structural information by supervised dynamic local focus.
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Searching for Effective Neural Extractive Summarization: What Works and What’s Next (P19-1)

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Challenge: Recent years have seen success in the use of deep neural networks on text summarization, but there is no clear understanding of why they perform so well or how they might be improved.
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Do Sentence Interactions Matter? Leveraging Sentence Level Representations for Fake News Classification (D19-53)

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Challenge: Existing methods to distinguish between trusted and fake news articles lack feature engineering . et al. (2009) define fake news as the one which deliberately exposes real-world individuals, organisations and events to ridicule.
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A Hierarchical Neural Attention-based Text Classifier (D18-1)

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Challenge: Existing hierarchical classification models are unable to handle large corpora and the number of categories increases with increasing corpus.
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Outcome: The proposed model performs better than or comparable to state-of-the-art hierarchical models at significantly lower computational cost while maintaining high interpretability.
Higher-Order Syntactic Attention Network for Longer Sentence Compression (N18-1)

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Challenge: Existing sentence compression methods do not handle syntactic features, causing performance degradation . et al. (2015) reported that the longer the input sentences are, the worse the performance becomes.
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