| Challenge: | Using deep learning models, we find that word embedding does not improve performance over simpler models. |
| Approach: | They propose to use sentence embedding to perform content selection across multiple domains . they propose to propose two alternative models that use auto-regressive sentence extraction . |
| Outcome: | The proposed models improve performance across news, personal stories, meetings, and medical articles. |
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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. |
| Approach: | They propose to use different types of model architectures to improve extractive summarization systems. |
| Outcome: | The proposed framework achieves state-of-the-art on CNN/DailyMail by a large margin based on observations and analysis. |
Exploring Content Selection in Summarization of Novel Chapters (2020.acl-main)
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| Challenge: | We focus on extractive summarization, which requires the creation of a gold-standard set of extractive summary summaries. |
| Approach: | They propose a new metric for aligning summary sentences with chapter sentences to create gold extracts. |
| Outcome: | The proposed method improves on previous methods and automatic metrics and a crowd-sourced pyramid analysis. |
What Have We Achieved on Text Summarization? (2020.emnlp-main)
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| Challenge: | Existing methods for text summarization have been investigated, but there are still gaps between them and human professionals. |
| Approach: | They analyze 8 major sources of errors on 10 representative summarization models manually. |
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A Closer Look at Data Bias in Neural Extractive Summarization Models (D19-54)
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| Challenge: | In this paper, we examine the generalization behaviour of summarization models . we propose several properties of datasets that matter for generalization . |
| Approach: | They propose several properties of datasets which matter for generalization of summarization models. |
| Outcome: | The proposed approach improves the state-of-the-art model by rethinking the model design process on a typical dataset. |
Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization (D18-1)
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| Challenge: | Existing approaches to summarize documents are not extractive and require an abstractive approach. |
| Approach: | They propose a novel abstractive model which is conditioned on the article’s topics and based entirely on convolutional neural networks. |
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Exploiting Discourse-Level Segmentation for Extractive Summarization (D19-54)
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| Challenge: | Existing approaches to extract summarize text are based on sentences as the elementary unit, but semantic segments containing supplementary information or descriptive details are often nonessential in the generated summaries. |
| Approach: | They propose to exploit discourse-level segmentation as a finer-grained means to more precisely pinpoint the core content in a document. |
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Summarization Evaluation in the Absence of Human Model Summaries Using the Compositionality of Word Embeddings (C18-1)
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| Challenge: | Existing summary evaluation methods rely on multiple model summaries to evaluate quality of summary outputs. |
| Approach: | They propose a new summary evaluation approach that does not require human model summaries . they exploit compositional capabilities of word embeddings to develop features . |
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Long-Span Summarization via Local Attention and Content Selection (2021.acl-long)
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| Challenge: | Transformer-based models are state-of-the-art for a wide range of natural language processing tasks, including document summarization. |
| Approach: | They exploit large pre-trained transformer-based models and address long-span dependencies in abstractive summarization using two methods: local self-attention; and explicit content selection. |
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Interactive Query-Assisted Summarization via Deep Reinforcement Learning (2022.naacl-main)
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| Challenge: | Existing systems that can perform interactive summarization cannot ingest the full document set or operate at sufficient speed for interactivity. |
| Approach: | They propose two deep reinforcement learning models for interactive summarization task . they use interactive session state and history to refrain from redundancy . |
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Summary Cloze: A New Task for Content Selection in Topic-Focused Summarization (D19-1)
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| Challenge: | Recent work on topic-focused summarization has focused on generic summarizing due to the size of the available datasets. |
| Approach: | They propose a method to study content selection in topic-focused summarization by narrowing the scope to a sentence-level task. |
| Outcome: | The proposed method can generate a large-scale summary cloze dataset from Wikipedia. |