| Challenge: | Existing frameworks for summary generation do not account for linguistic preferences of the specific audience who will consume the summary. |
| Approach: | They propose a neural framework to generate summary constrained to a vocabulary-defined linguistic preferences of a target audience. |
| Outcome: | The proposed approach generates understandable summaries with simpler words and readable summary with shorter words against a state-of-the-art word embedding based lexical substitution algorithm. |
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| Challenge: | Experimental results show that our proposed framework generates fluent and factually consistent summaries under various planning controls using both objective metrics and human evaluations. |
| Approach: | They propose a controllable neural generation framework that can guide dialogue summarization with personal named entity planning. |
| Outcome: | The proposed framework generates fluent and factually consistent summaries under various planning controls using objective metrics and human evaluations. |
Jointly Learning Guidance Induction and Faithful Summary Generation via Conditional Variational Autoencoders (2022.findings-naacl)
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| Challenge: | Existing methods for abstractive summarization generate factual consistency summaries with a high level of accuracy and coherence. |
| Approach: | They propose a framework that induces the guidance information and generates summary equipment with the guidance synchronously. |
| Outcome: | The proposed framework generates fluent summaries with no constraint on the words and phrases, and is more faithful than the existing state-of-the-art approaches. |
Enriching and Controlling Global Semantics for Text Summarization (2021.emnlp-main)
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| Challenge: | Abstractive summarization models have been proven effective in creating fluent and informative summaries, but they suffer from the short-range dependency problem, causing them to produce summary that miss the key points of document. |
| Approach: | They propose a neural topic model empowered with normalizing flow to capture global semantics of the document and integrate them into the summarization model. |
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Inference Time Style Control for Summarization (2021.naacl-main)
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| Challenge: | Existing methods to generate summaries of different styles without training separate models are lacking parallel data and expensive (re)training. |
| Approach: | They propose two methods that can be deployed during summary decoding on any pre-trained Transformer-based summarization model. |
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Planning with Learned Entity Prompts for Abstractive Summarization (2021.tacl-1)
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| Challenge: | a simple but flexible mechanism is used to ground the generation of abstractive summaries. |
| Approach: | They propose a mechanism to learn an intermediate plan to ground the generation of abstractive summaries. |
| Outcome: | The proposed model outperforms state-of-the-art methods for faithfulness on CNN and BillSum. |
Guided Neural Language Generation for Abstractive Summarization using Abstract Meaning Representation (D18-1)
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| Challenge: | Recent work on abstractive summarization has made progress with neural encoder-decoder architectures, but these models lack explicit semantic modeling of the source document and its summary. |
| Approach: | They extend previous work on abstractive summarization using Abstract Meaning Representation (AMR) with a neural language generation stage which they guide using the source document. |
| Outcome: | The proposed approach improves summarization performance by 7.4 and 10.5 points in ROUGE-2 using gold standard AMR parses and parses obtained from an off-the-shelf parser respectively. |
Generating Summaries with Topic Templates and Structured Convolutional Decoders (P19-1)
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| Challenge: | Existing neural generation approaches create multi-sentence text as a single sequence . Existing approaches create multiple sentences as if they were a sequence based on content structure . |
| Approach: | They propose a structured convolutional decoder that is guided by the content structure of target summaries. |
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Bottom-Up Abstractive Summarization (D18-1)
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| Challenge: | Existing approaches to summarize text using end-to-end content selectors have had mixed success in content selection, for example copying full sentences from the source document. |
| Approach: | They propose to use content selectors to over-determine phrases in a source document that should be part of the summary. |
| Outcome: | The proposed model over-determines phrases in a source document that should be part of the summary while generating fluent summaries. |
Conditional Neural Generation using Sub-Aspect Functions for Extractive News Summarization (2020.findings-emnlp)
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| Challenge: | Recent advances in text summarization have overcome position bias in news articles . however, there are long-standing, unresolved challenges in extractive summarizing . |
| Approach: | They propose a neural framework that can flexibly control summary generation by introducing a set of sub-aspect functions. |
| Outcome: | The proposed framework can flexibly control summary generation by introducing sub-aspect functions . extracted summaries with minimal position bias are comparable with standard models . |
Sentence-Level Content Planning and Style Specification for Neural Text Generation (D19-1)
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| Challenge: | Recent advances in text generation systems often produce incoherent and unfaithful outputs . a novel automated text generation system takes into account content selection, text planning, and surface realization. |
| Approach: | They propose an end-to-end trained two-step text generation model that considers sentence-level content planners and language styles. |
| Outcome: | The proposed model outperforms competing models in three domains with diverse topics and varying language styles. |