| Challenge: | Currently, most of the neural extractive summarization systems score and extract sentences individually and model the relationship between sentences. |
| Approach: | They propose to instantiate a neural extractive summarization task as a semantic text matching problem and use it to match a source document and candidate summaries in a semantic space. |
| Outcome: | The proposed framework is faster and more efficient than existing frameworks. |
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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. |
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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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Neural Extractive Text Summarization with Syntactic Compression (D19-1)
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| Challenge: | Recent approaches to summarization are either selection-based extraction or generation-based abstraction. |
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Reading Like HER: Human Reading Inspired Extractive Summarization (D19-1)
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| Challenge: | Existing methods for extracting text summarization are abstractive and extractive. |
| Approach: | They propose a novel approach for extractive summarization by simulating two stages . they adopt a convolutional neural network to encode gist of paragraphs for rough reading . |
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| Challenge: | Sentence-level extractive text summarization is difficult to model the importance of sentences. |
| Approach: | They propose a Frame Semantic-Enhanced Sentence Modeling for Extractive Summarization that leverages Frame semantics to model sentences from both intra-sentence level and inter-sentent level. |
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Extractive Summarization with Text Generator (2024.naacl-long)
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| Challenge: | Existing extractive systems lack gold training signals, thereby hindering learning of extractive models. |
| Approach: | They propose to use text generators to train extractive summarizers by approximating outputs of abstractive summaries. |
| Outcome: | The proposed method can be used to train extractive summarizers without training . it is shown that the approximated summaries correlate positively with the auxiliary summary outputs. |
Neural Document Summarization by Jointly Learning to Score and Select Sentences (P18-1)
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| Challenge: | Sentence scoring and sentence selection are two main steps in extractive document summarization systems. |
| Approach: | They propose an end-to-end neural network framework for extractive document summarization by jointly learning to score and select sentences. |
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Jointly Extracting and Compressing Documents with Summary State Representations (N19-1)
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| Challenge: | Text summarization is an important NLP problem with a wide range of applications in data-driven industries. |
| Approach: | They propose a neural model that extracts sentences from a document and compresses them. |
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A Set Prediction Network For Extractive Summarization (2023.findings-acl)
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| Challenge: | Recent approaches to extracting salient sentences from source document are naive and lack dependencies between sentences. |
| Approach: | They propose a set prediction network to detect redundancy relationship between sentences . they use a non-autoregressive decoder to predict sentences in parallel . |
| Outcome: | The proposed method outperforms previous state-of-the-art models on extracted summary datasets. |
Align then Summarize: Automatic Alignment Methods for Summarization Corpus Creation (2020.lrec-1)
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| Challenge: | Summarizing text is not a straightforward task. |
| Approach: | They propose to use automated transcriptions to generate reports from automatic transcriptions as a dataset for neural summarization. |
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