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MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance
TLDR
This paper investigates strategies to encode system and reference texts to devise a metric that shows a high correlation with human judgment of text quality and validate the new metric, namely MoverScore, on a number of text generation tasks.
Toward Abstractive Summarization Using Semantic Representations
TLDR
This work focuses on the graph-tograph transformation that reduces the source semantic graph into a summary graph, making use of an existing AMR parser and assuming the eventual availability of an AMR-totext generator.
Insertion, Deletion, or Substitution? Normalizing Text Messages without Pre-categorization nor Supervision
TLDR
This paper proposes a unified letter transformation approach that requires neither pre-categorization nor human supervision and significantly outperformed the state-of-the-art deletion-based abbreviation system and the jazzy spell checker.
A Broad-Coverage Normalization System for Social Media Language
TLDR
A cognitively-driven normalization system that integrates different human perspectives in normalizing the nonstandard tokens, including the enhanced letter transformation, visual priming, and string/phonetic similarity is proposed.
Adapting the Neural Encoder-Decoder Framework from Single to Multi-Document Summarization
TLDR
An initial investigation into a novel adaptation method that exploits the maximal marginal relevance method to select representative sentences from multi-document input, and leverages an abstractive encoder-decoder model to fuse disparate sentences to an Abstractive summary.
Scoring Sentence Singletons and Pairs for Abstractive Summarization
TLDR
This proposed framework attempts to model human methodology by selecting either a single sentence or a pair of sentences, then compressing or fusing the sentence(s) to produce a summary sentence.
Crowdsourcing Annotations for Websites' Privacy Policies: Can It Really Work?
TLDR
The results suggest that, if carefully deployed, crowdsourcing can indeed result in the generation of non-trivial annotations and can also help identify elements of ambiguity in policies.
Analyzing Sentence Fusion in Abstractive Summarization
TLDR
This paper analyzes the outputs of five state-of-the-art abstractive summarizers, focusing on summary sentences that are formed by sentence fusion, and reveals that system sentences are mostly grammatical, but often fail to remain faithful to the original article.
Improving the Similarity Measure of Determinantal Point Processes for Extractive Multi-Document Summarization
TLDR
This paper seeks to strengthen a DPP-based method for extractive multi-document summarization by presenting a novel similarity measure inspired by capsule networks, and shows that the DPP system with improved similarity measure performs competitively, outperforming strong summarization baselines on benchmark datasets.
Structure-Infused Copy Mechanisms for Abstractive Summarization
TLDR
This paper presents structure-infused copy mechanisms to facilitate copying important words and relations from the source sentence to summary sentence, which naturally combines source dependency structure with the copy mechanism of an abstractive sentence summarizer.
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