REVIEW 1 cited by
SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
We present SummaRuNNer, a Recurrent Neural Network (RNN) based sequence model for extractive summarization of documents and show that it achieves performance better than or comparable to state-of-the-art. Our model has the additional advantage of being very interpretable, since it allows visualization of its predictions broken up by abstract features such as information content, salience and novelty. Another novel contribution of our work is abstractive training of our extractive model that can train on human generated reference summaries alone, eliminating the need for sentence-level extractive labels.
Forward citations
Cited by 1 Pith paper
-
QwenLong-CPRS: Towards $\infty$-LLMs with Dynamic Context Optimization
QwenLong-CPRS is a 7B instruction-guided compressor that shrinks long contexts to query-relevant spans, boosting downstream LLM accuracy and cutting prefill cost.
Discussion (0). Continue with ORCID to comment.