Search Results for author:
Found 15 papers, 13 papers with code
OASum: Large-Scale Open Domain Aspect-based Summarization
Specifically, zero/few-shot and fine-tuning results show that the model pre-trained on our corpus demonstrates a strong aspect or query-focused generation ability compared with the backbone model.
NarraSum: A Large-Scale Dataset for Abstractive Narrative Summarization
To encourage research in this direction, we propose NarraSum, a large-scale narrative summarization dataset.
Toward Unifying Text Segmentation and Long Document Summarization
The problem is only exacerbated by a lack of segmentation in transcripts of audio/video recordings.
Ranked #5 on
Text Summarization
on Pubmed
Salience Allocation as Guidance for Abstractive Summarization
Recent literature adds extractive summaries as guidance for abstractive summarization models to provide hints of salient content and achieves better performance.
Ranked #6 on
Abstractive Text Summarization
on CNN / Daily Mail
Towards Abstractive Grounded Summarization of Podcast Transcripts
Summarization of podcast transcripts is of practical benefit to both content providers and consumers.
Learning-by-Narrating: Narrative Pre-Training for Zero-Shot Dialogue Comprehension
Comprehending a dialogue requires a model to capture diverse kinds of key information in the utterances, which are either scattered around or implicitly implied in different turns of conversations.
A New Approach to Overgenerating and Scoring Abstractive Summaries
We propose a new approach to generate multiple variants of the target summary with diverse content and varying lengths, then score and select admissible ones according to users' needs.
Ranked #10 on
Text Summarization
on GigaWord
CATE: Computation-aware Neural Architecture Encoding with Transformers
Our experiments show that CATE is beneficial to the downstream search, especially in the large search space.
Automatic Summarization of Open-Domain Podcast Episodes
Instead, we investigate several less-studied aspects of neural abstractive summarization, including (i) the importance of selecting important segments from transcripts to serve as input to the summarizer; (ii) striking a balance between the amount and quality of training instances; (iii) the appropriate summary length and start/end points.
Better Highlighting: Creating Sub-Sentence Summary Highlights
Amongst the best means to summarize is highlighting.
Joint Parsing and Generation for Abstractive Summarization
If generating a word can introduce an erroneous relation to the summary, the behavior must be discouraged.
Ranked #26 on
Text Summarization
on GigaWord
Controlling the Amount of Verbatim Copying in Abstractive Summarization
In this paper, we present a neural summarization model that, by learning from single human abstracts, can produce a broad spectrum of summaries ranging from purely extractive to highly generative ones.
Ranked #12 on
Text Summarization
on GigaWord
Scoring Sentence Singletons and Pairs for Abstractive Summarization
There is thus a crucial gap between sentence selection and fusion to support summarizing by both compressing single sentences and fusing pairs.
Adapting the Neural Encoder-Decoder Framework from Single to Multi-Document Summarization
Generating a text abstract from a set of documents remains a challenging task.
Structure-Infused Copy Mechanisms for Abstractive Summarization
In this paper, we present structure-infused copy mechanisms to facilitate copying important words and relations from the source sentence to summary sentence.
Ranked #33 on
Text Summarization
on GigaWord
