Learning-Based Single-Document Summarization with Compression and Anaphoricity Constraints

Durrett, Greg; Berg-Kirkpatrick, Taylor; Klein, Dan

doi:10.18653/v1/p16-1188

Cited by 130 publications

(144 citation statements)

References 37 publications

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“…The LEAD-3 baseline (selecting the first three sentences in each document as the summary) is extremely difficult to beat on CNN/DailyMail (Narayan et al, 2018b,a), which implies that salient information is mostly concentrated in the beginning of a document. NYT writers follow less prescriptive guidelines 2 , and as a result salient information is distributed more evenly in the course of an article (Durrett et al, 2016). We therefore view the NYT annotated corpus (Sandhaus, 2008) as complementary to CNN/DailyMail in terms of evaluating the model's ability of finding salient information.…”

Section: Datasetsmentioning

confidence: 99%

Sentence Centrality Revisited for Unsupervised Summarization

Zheng¹,

Lapata²

2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

145

141

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Single document summarization has enjoyed renewed interest in recent years thanks to the popularity of neural network models and the availability of large-scale datasets. In this paper we develop an unsupervised approach arguing that it is unrealistic to expect large-scale and high-quality training data to be available or created for different types of summaries, domains, or languages. We revisit a popular graph-based ranking algorithm and modify how node (aka sentence) centrality is computed in two ways: (a) we employ BERT, a state-of-the-art neural representation learning model to better capture sentential meaning and (b) we build graphs with directed edges arguing that the contribution of any two nodes to their respective centrality is influenced by their relative position in a document. Experimental results on three news summarization datasets representative of different languages and writing styles show that our approach outperforms strong baselines by a wide margin. 1(2) Clara Eaglen, from the royal national in-

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Section: Datasetsmentioning

confidence: 99%

Sentence Centrality Revisited for Unsupervised Summarization

Zheng¹,

Lapata²

2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

145

141

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“…On the CNN/Daily Mail and DUC-2002 dataset, we use standard ROUGE-1, ROUGE-2, and ROUGE- L (Lin, 2004) on full length F 1 with stemming as previous work did (Nallapati et al, 2017;See et al, 2017;Chen and Bansal, 2018). On NYT50 dataset, following Durrett et al (2016) and Paulus et al (2018), we used the limited length ROUGE recall metric, truncating the generated summary to the length of the ground truth summary. Table 1 shows the experimental results on CNN/Daily Mail dataset, with extractive models in the top block and abstractive models in the bottom block.…”

Section: Discussionmentioning

confidence: 99%

“…The New York Times dataset also consists of many news articles. We followed the dataset splits of Durrett et al (2016); 100,834 for training and…”

Section: Datasetsmentioning

confidence: 99%

Summary Level Training of Sentence Rewriting for Abstractive Summarization

Bae¹,

Kim²,

Kim³

et al. 2019

Proceedings of the 2nd Workshop on New Frontiers in Summarization

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As an attempt to combine extractive and abstractive summarization, Sentence Rewriting models adopt the strategy of extracting salient sentences from a document first and then paraphrasing the selected ones to generate a summary. However, the existing models in this framework mostly rely on sentence-level rewards or suboptimal labels, causing a mismatch between a training objective and evaluation metric. In this paper, we present a novel training signal that directly maximizes summary-level ROUGE scores through reinforcement learning. In addition, we incorporate BERT into our model, making good use of its ability on natural language understanding. In extensive experiments, we show that a combination of our proposed model and training procedure obtains new state-of-the-art performance on both CNN/Daily Mail and New York Times datasets. We also demonstrate that it generalizes better on DUC-2002 test set.

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“…We extracted the first 3 sentences for CNN documents and the first 4 sentences for DailyMail (Narayan et al, 2018b). Following previous work (Durrett, Berg-Kirkpatrick, & Klein, 2016;Paulus et al, 2018), we obtained lead summaries based on the first 100 words for NY Times documents. For Newsroom, we extracted the first 2 sentences to form the lead summaries.…”

Section: How Abstractive Is Xsum?mentioning

confidence: 99%

What is this Article about? Extreme Summarization with Topic-aware Convolutional Neural Networks

Narayan¹,

Cohen²,

Lapata³

2019

jair

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We introduce extreme summarization, a new single-document summarization task which aims at creating a short, one-sentence news summary answering the question "What is the article about?". We argue that extreme summarization, by nature, is not amenable to extractive strategies and requires an abstractive modeling approach. In the hope of driving research on this task further: (a) we collect a real-world, large scale dataset by harvesting online articles from the British Broadcasting Corporation (BBC); and (b) propose a novel abstractive model which is conditioned on the article's topics and based entirely on convolutional neural networks. We demonstrate experimentally that this architecture captures long-range dependencies in a document and recognizes pertinent content, outperforming an oracle extractive system and state-of-the-art abstractive approaches when evaluated automatically and by humans on the extreme summarization dataset. 1 Summary: A man and a child have been killed after a light aircraft made an emergency landing on a beach in Portugal.Document: Authorities said the incident took place on Sao Joao beach in Caparica, south-west of Lisbon. The National Maritime Authority said a middle-aged man and a young girl died after they were unable to avoid the plane. The plane's only two occupants were unharmed, it added. The Diario de Noticias newspaper quoted an eyewitness who said the plane had been flying at a low altitude over the beach, although he did not realise anything was wrong until other beachgoers began running. One young witness told Reuters news agency: "I was near the water when I saw the plane. I called my parents, the plane fell on the sand and ran over two people, fatally hurting them and another was injured, I think, but I'm not sure, people were running away." "The plane is still there, but the ambulances arrived quickly. I think maybe the fuel ran out because I find it weird that it landed on the beach." Other reports said the victims had been sunbathing when the plane made its emergency landing. The Associated Press news agency said the girl who died had been with her parents, who were unhurt. The agency quoted witnesses from local television broadcasts. Joao Quadros, who was on the beach, tweeted photos of the aftermath, saying the plane had passed by his son by a matter of metres. There had been no noise, he said. Video footage from the scene carried by local broadcasters showed a small recreational plane parked on the sand, apparently intact and surrounded by beachgoers and emergency workers. One wing seemed to be misaligned in those photos. The cause of the emergency landing remains unclear.2. Experiments with Transformer architectures are outside the scope of this paper. Recent work (Perez-Beltrachini, Liu, & Lapata, 2019) on multiodocument summarization shows that Transformer-based models perform on par with their convolutional alternatives.

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Learning-Based Single-Document Summarization with Compression and Anaphoricity Constraints

Cited by 130 publications

References 37 publications

Sentence Centrality Revisited for Unsupervised Summarization

Sentence Centrality Revisited for Unsupervised Summarization

Summary Level Training of Sentence Rewriting for Abstractive Summarization

What is this Article about? Extreme Summarization with Topic-aware Convolutional Neural Networks

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