Papers with abstractive model
A Hybrid Approach to Cross-lingual Product Review Summarization (2022.emnlp-industry)
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| Challenge: | Existing methods for summarizing product reviews with thousands of reviews are inefficient and time consuming. |
| Approach: | They propose an unsupervised extractive step and a supervised abstractive step to generate a short summary in any language. |
| Outcome: | The proposed model is as good as human written summaries in coherence, informativeness, non-redundancy, and fluency as human summary summators. |
A Unified Model for Extractive and Abstractive Summarization using Inconsistency Loss (P18-1)
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| Challenge: | extractive models can obtain sentence-level attention with high ROUGE scores but less readable. abstractive models generate novel words and phrases not copied from the source text. |
| Approach: | They propose to combine extractive and abstractive models to achieve a unified model that generates readable paragraphs with word-level attention. |
| Outcome: | The proposed model achieves state-of-the-art ROUGE scores while being the most informative and readable summarization on the CNN/Daily Mail dataset in a solid human evaluation. |
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. |
| Outcome: | The proposed model outperforms an oracle extractive system and state-of-the-art abstractive approaches when evaluated automatically and by humans. |
Generating Multiple-Length Summaries via Reinforcement Learning for Unsupervised Sentence Summarization (2022.findings-emnlp)
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| Challenge: | Existing models to summarize texts without ground-truth summaries are extractive, which remove words from texts and thus are less flexible than abstractive models. |
| Approach: | They propose an unsupervised model that extracts words from texts and makes them mutually enhance each other. |
| Outcome: | The proposed model outperforms both abstractive and extractive models, while generating new words not contained in input texts. |
Unsupervised Abstractive Summarization of Bengali Text Documents (2021.eacl-main)
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Radia Rayan Chowdhury, Mir Tafseer Nayeem, Tahsin Tasnim Mim, Md. Saifur Rahman Chowdhury, Taufiqul Jannat
| Challenge: | Abstractive summarization systems are difficult to perform due to the unavailability of the parallel data for low-resource languages like Bengali. |
| Approach: | They propose a graph-based unsupervised abstractive summarization system in Bengali text documents that requires only a Part-Of-Speech (POS) tagger and a pre-trained language model trained on Bengali texts. |
| Outcome: | The proposed system outperforms baselines without human-annotated reference summaries on a human-random dataset with Bengali text. |
Informative and Controllable Opinion Summarization (2021.eacl-main)
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| Challenge: | Existing methods for opinion summarization use a two-stage extractive and abstractive approach to generate summaries for reviews of a specific target. |
| Approach: | They propose a framework for opinion summarization that condenses all input reviews into multiple dense vectors which serve as input to an abstractive model. |
| Outcome: | The proposed framework produces more informative summaries and allows to take user preferences into account using a zero-shot customization technique. |
To Point or Not to Point: Understanding How Abstractive Summarizers Paraphrase Text (2021.findings-acl)
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| Challenge: | Abstractive summarization models have seen great improvements in recent years, but there is limited understanding of the strategies different models employ and how they relate their understanding of language. |
| Approach: | They characterize how one popular abstractive model uses an explicit copy/generation switch to control its level of abstraction vs extraction . they find that abstractive summarization models lack the semantic understanding necessary to generate paraphrases that are both abstractive and faithful to the source document. |
| Outcome: | The proposed model uses syntactic boundaries to truncate sentences that are often copied verbatim. |
ELI5: Long Form Question Answering (P19-1)
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| Challenge: | Existing question answering datasets provide extractive or short answers, but less attention has been paid to open-ended questions that require explanations. |
| Approach: | They present a large-scale corpus for long form question answering . they use a Reddit forum to provide elaborate answers to open-ended questions . |
| Outcome: | The proposed model outperforms Seq2Seq, language modeling, and other models in human evaluations. |
Learning to Plan and Generate Text with Citations (2024.acl-long)
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Constanza Fierro, Reinald Kim Amplayo, Fantine Huot, Nicola De Cao, Joshua Maynez, Shashi Narayan, Mirella Lapata
| Challenge: | Large language models (LLMs) are increasingly useful in information-seeking scenarios, ranging from answering simple questions to generating responses to search-like queries. |
| Approach: | They propose to use plan-based models to improve faithfulness, grounding, and controllability of generated content and its organization. |
| Outcome: | The proposed models improve faithfulness, grounding, and controllability of generated content and its organization. |
GECSum: Generative Evaluation-Driven Sequence Level Contrastive Learning for Abstractive Summarization (2024.lrec-main)
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| Challenge: | Abstractive summarization is a technique in natural language processing that involves generating a summary of a source document by creating new sentences and phrases. |
| Approach: | They propose a sequence-level contrastive learning framework that leverages the semantic understanding capabilities of the abstractive model itself to evaluate summary in reference-based settings. |
| Outcome: | The proposed framework outperforms the state-of-the-art in four summarization datasets. |