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Register a new userEnd-to-end Semantics-based Summary Quality Assessment for Single-document Summarization
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Added by
Forrest Bao
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Canonical automatic summary evaluation metrics, such as ROUGE, suffer from two drawbacks. First, semantic similarity and linguistic quality are not captured well. Second, a reference summary, which is expensive or impossible to obtain in many cases, is needed. Existing efforts to address the two drawbacks are done separately and have limitations. To holistically address them, we introduce an end-to-end approach for summary quality assessment by leveraging sentence or document embedding and introducing two negative sampling approaches to create training data for this supervised approach. The proposed approach exhibits promising results on several summarization datasets of various domains including news, legislative bills, scientific papers, and patents. When rating machine-generated summaries in TAC2010, our approach outperforms ROUGE in terms of linguistic quality, and achieves a correlation coefficient of up to 0.5702 with human evaluations in terms of modified pyramid scores. We hope our approach can facilitate summarization research or applications when reference summaries are infeasible or costly to obtain, or when linguistic quality is a focus.
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End-to-end (E2E) systems have achieved competitive results compared to conventional hybrid hidden Markov model (HMM)-deep neural network based automatic speech recognition (ASR) systems. Such E2E systems are attractive due to the lack of dependence on alignments between input acoustic and output grapheme or HMM state sequence during training. This paper explores the design of an ASR-free end-to-end system for text query-based keyword search (KWS) from speech trained with minimal supervision. Our E2E KWS system consists of three sub-systems. The first sub-system is a recurrent neural network (RNN)-based acoustic auto-encoder trained to reconstruct the audio through a finite-dimensional representation. The second sub-system is a character-level RNN language model using embeddings learned from a convolutional neural network. Since the acoustic and text query embeddings occupy different representation spaces, they are input to a third feed-forward neural network that predicts whether the query occurs in the acoustic utterance or not. This E2E ASR-free KWS system performs respectably despite lacking a conventional ASR system and trains much faster.
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