Papers by Logan Lebanoff
Automatic Detection of Vague Words and Sentences in Privacy Policies (D18-1)
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| Challenge: | a recent study has raised concerns over privacy policies' opaqueness . lack of clarity in privacy policies can lead to undesired ads and privacy breaches . |
| Approach: | They propose to analyze the semantics of vague words and sentences and use them to identify vague content in privacy policies. |
| Outcome: | The proposed methods are effective and provide suggestions for improving privacy policies. |
Abstract Meaning Representation for Multi-Document Summarization (C18-1)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic representation of natural language based on linguistic theory . |
| Approach: | They propose to use Abstract Meaning Representation (AMR) as a content representation. |
| Outcome: | The proposed framework is fully data-driven and flexible. |
Analyzing Sentence Fusion in Abstractive Summarization (D19-54)
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Logan Lebanoff, John Muchovej, Franck Dernoncourt, Doo Soon Kim, Seokhwan Kim, Walter Chang, Fei Liu
| Challenge: | Abstractive summarization systems struggle to combine information from multiple sources, resulting in poor grammar and incorrect facts. |
| Approach: | They analyze the outputs of five abstractive summarization systems and examine their grammatical accuracy and faithfulness. |
| Outcome: | The proposed summarization systems are able to combine information from multiple sources, but they often fail to remain faithful to the original document. |
A Cascade Approach to Neural Abstractive Summarization with Content Selection and Fusion (2020.aacl-main)
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| Challenge: | Existing systems that perform content selection and surface realization are not able to provide sufficient training data for news summarization. |
| Approach: | They propose to use a cascade architecture to perform content selection and surface realization together to generate abstracts. |
| Outcome: | The proposed architecture outperforms or outranks existing systems in terms of content selection and surface realization. |
Adapting the Neural Encoder-Decoder Framework from Single to Multi-Document Summarization (D18-1)
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| Challenge: | Existing methods to summarize short texts using a neural encoder-decoder are limited and expensive to obtain. |
| Approach: | They propose to use a maximal marginal relevance method to select representative sentences from multi-document input and leverage an abstractive encoder-decoder model to fuse disparate sentences to an abstract. |
| Outcome: | The proposed method compares favorably to state-of-the-art extractive and abstractive approaches judged by automatic metrics and human assessors. |
Improving the Similarity Measure of Determinantal Point Processes for Extractive Multi-Document Summarization (P19-1)
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| Challenge: | Despite the empirical success of multi-document summarization, most datasets remain small and the cost of hiring hu-1 is prohibitive. |
| Approach: | They propose a novel method for extractive multi-document summarization that measures redundancy between a pair of sentences based on surface form and semantic information. |
| Outcome: | The proposed method outperforms baseline methods on benchmark datasets and is particularly useful for documents created by multiple authors containing redundant yet lexically diverse expressions. |
Understanding Points of Correspondence between Sentences for Abstractive Summarization (2020.acl-srw)
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| Challenge: | Using points of correspondence, fusion systems are difficult for abstractive summarizers because of their complexity. |
| Approach: | They propose to model points of correspondence between disparate sentences by combining documents, source and fusion sentences, and human annotations of points of correspondance between sentences. |
| Outcome: | The proposed model bridges the gap between coreference resolution and summarization by using human annotations of points of correspondence between sentences. |
Scoring Sentence Singletons and Pairs for Abstractive Summarization (P19-1)
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Logan Lebanoff, Kaiqiang Song, Franck Dernoncourt, Doo Soon Kim, Seokhwan Kim, Walter Chang, Fei Liu
| Challenge: | Existing methods for summarizing content from single sentences are inadequately understood. |
| Approach: | They propose to combine singletons and pairs to create a summarizing sentence . they use a dataset of human-written abstracts to examine human-writing methods . |
| Outcome: | The proposed framework is based on human-written abstracts from three large datasets. |
Learning to Fuse Sentences with Transformers for Summarization (2020.emnlp-main)
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| Challenge: | Abstractive summarization systems that fuse sentences are not rewarded for correctly fusing sentences. |
| Approach: | They propose to leverage the knowledge of points of correspondence between sentences to enhance their ability to fuse sentences. |
| Outcome: | The proposed algorithms improve the ability of the proposed summarization systems to fuse sentences and show that they can fuse sentences in a way that retains the original meaning. |