Proceedings of the 2nd Workshop on New Frontiers in Summarization
Answering Naturally: Factoid to Full length Answer Generation (D19-54)
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| Challenge: | Factoid question answering systems extract answers for a question from passages, which are usually short spans of text . but, these spans would result in an unnatural reading experience in a conversational system . a pointer generator based full-length answer generator can be used with most QA systems . |
| Approach: | They propose a pointer generator based full-length answer generator which can be used with most QA systems. |
| Outcome: | The proposed system generates full length answer without relying on passage from which it was extracted. |
Summary Level Training of Sentence Rewriting for Abstractive Summarization (D19-54)
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| Challenge: | Existing models rely on sentence-level rewards or suboptimal labels to achieve summary-level ROUGE scores. |
| Approach: | They propose a model that extracts salient sentences from a document and paraphrases them to generate a summary. |
| Outcome: | The proposed model improves on CNN/Daily Mail and New York Times datasets. |
Abstractive Timeline Summarization (D19-54)
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| Challenge: | Prior approaches to TLS focus on extractive methods, which generate extractive timelines . a study with human judges shows that our abstractive system also produces output that is easy to read and understand. |
| Approach: | They propose an abstractive timeline summarization system that is unsupervised . their system outperforms extractive systems in terms of ROUGE scores . |
| Outcome: | The proposed system outperforms extractive systems in terms of ROUGE scores . it produces output that is easy to read and understand, the authors say . |
Learning to Create Sentence Semantic Relation Graphs for Multi-Document Summarization (D19-54)
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| Challenge: | Existing methods for summarizing documents rely on hand-crafted features or additional annotated data. |
| Approach: | They propose a method that makes use of two types of sentence embeddings . the method uses universal embeddable and domain-specific embeddible features . |
| Outcome: | The proposed method achieves competitive results on two types of summary, consisting of 665 bytes and 100 words. |
Unsupervised Aspect-Based Multi-Document Abstractive Summarization (D19-54)
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| Challenge: | Existing methods for opinion summarization are expensive and do not deal with contradictory statements. |
| Approach: | They propose an unsupervised abstractive summarization neural system that generates short summaries of reviews in a vector space. |
| Outcome: | The proposed system can generate short summaries of user-generated reviews in a short paragraph, while nobody reads all reviews. |
BillSum: A Corpus for Automatic Summarization of US Legislation (D19-54)
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| Challenge: | In the US Congress, over 10,000 bills are introduced each year, with state legislatures introducing tens of thousands of bills. |
| Approach: | They introduce the first dataset for summarizing US Congressional and California state bills . they demonstrate that models built on Congressional bills can be used to summarize California billa . |
| Outcome: | The proposed summarization methods can be applied to states without human-written summaries. |
An Editorial Network for Enhanced Document Summarization (D19-54)
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| Challenge: | Existing extractive and abstractive summarization methods are less fluent, coherent and readable, whereas extractive methods are sensitive to vocabulary size, making them more difficult to train and generalize. |
| Approach: | They propose an approach which uses a combination of extractive and abstractive methods to combine a given sequence of sentences into a short version. |
| Outcome: | The proposed method is compared with state-of-the-art methods using extractive-only or abstractive- only baselines. |
Towards Annotating and Creating Summary Highlights at Sub-sentence Level (D19-54)
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| Challenge: | Creating summary highlights at the sub-sentence level is desirable because sub-entrances are more concise than whole sentences. |
| Approach: | They propose to annotate summary-worthy sub-sentences and teach classifiers to do the same . they frame task as selecting important sentences and identifying a single most informative textual unit from each sentence. |
| Outcome: | The proposed method reduces the complexity involved in sentence compression by reducing the number of words and phrases deleted. |
SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization (D19-54)
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| Challenge: | Existing work on abstractive dialogue summarizations has focused on news summarizing but there is no such comprehensive dataset. |
| Approach: | They propose to use a chat-dialogues corpus with abstractive dialogue summaries to generate a short version of text that covers the main points succinctly. |
| Outcome: | The proposed dataset achieves higher ROUGE scores than the model-generated summaries of news, compared with human evaluators' judgement. |
A Closer Look at Data Bias in Neural Extractive Summarization Models (D19-54)
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| Challenge: | In this paper, we examine the generalization behaviour of summarization models . we propose several properties of datasets that matter for generalization . |
| Approach: | They propose several properties of datasets which matter for generalization of summarization models. |
| Outcome: | The proposed approach improves the state-of-the-art model by rethinking the model design process on a typical dataset. |
Global Voices: Crossing Borders in Automatic News Summarization (D19-54)
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| Challenge: | a crowd-sourced dataset is needed to evaluate cross-lingual summarization methods . human-written summarizing is expensive and difficult to design for humans . |
| Approach: | They construct a multilingual dataset for evaluating cross-lingual summarization methods . they use social-network descriptions of news articles to extract evaluation data . |
| Outcome: | The proposed dataset compares a translate-then-summarize approach with baselines in 15 languages. |
Multi-Document Summarization with Determinantal Point Processes and Contextualized Representations (D19-54)
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| Challenge: | Determinantal point processes (DPP) is one of the best performing techniques for extractive summarization. |
| Approach: | They propose to combine determinantal point processes with surface indicators for effective identification of summary-worthy sentences. |
| Outcome: | The determinantal point processes (DPP) framework is one of the best performing in summarization competitions. |
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. |
Summarizing Relationships for Interactive Concept Map Browsers (D19-54)
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| Challenge: | Concept maps are visual summaries, structured as directed graphs . initial attempts to generate static summary models focused on static summarization . however, in interactive settings, users will need to dynamically query relationships . |
| Approach: | They propose a model which returns a labeled summary of a query concept for display in a visual interface. |
| Outcome: | The proposed model can summarize relationships between two query concepts in a visual network . it is based on a new dataset, and is trained on the dataset . |
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. |
| Outcome: | The proposed method improves extractive summarization performance on CNN/Daily Mail dataset. |