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Found 28 papers, 7 papers with code
Multi-Document Summarization via Discriminative Summary Reranking
However, according to our quantitative analysis, none of the existing summarization models can always produce high-quality summaries for different document sets, and even a summarization model with good overall performance may produce low-quality summaries for some document sets.
TGSum: Build Tweet Guided Multi-Document Summarization Dataset
Both informativeness and readability of the collected summaries are verified by manual judgment.
AttSum: Joint Learning of Focusing and Summarization with Neural Attention
Query relevance ranking and sentence saliency ranking are the two main tasks in extractive query-focused summarization.
Improving Multi-Document Summarization via Text Classification
Developed so far, multi-document summarization has reached its bottleneck due to the lack of sufficient training data and diverse categories of documents.
Joint Copying and Restricted Generation for Paraphrase
In this paper, we develop a novel Seq2Seq model to fuse a copying decoder and a restricted generative decoder.
DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset
We develop a high-quality multi-turn dialog dataset, DailyDialog, which is intriguing in several aspects.
Faithful to the Original: Fact Aware Neural Abstractive Summarization
While previous abstractive summarization approaches usually focus on the improvement of informativeness, we argue that faithfulness is also a vital prerequisite for a practical abstractive summarization system.
Ranked #22 on
Text Summarization
on GigaWord
Retrieve, Rerank and Rewrite: Soft Template Based Neural Summarization
Most previous seq2seq summarization systems purely depend on the source text to generate summaries, which tends to work unstably.
Ranked #23 on
Text Summarization
on GigaWord
BASS: Boosting Abstractive Summarization with Unified Semantic Graph
Abstractive summarization for long-document or multi-document remains challenging for the Seq2Seq architecture, as Seq2Seq is not good at analyzing long-distance relations in text.
General and Domain Adaptive Chinese Spelling Check with Error Consistent Pretraining
However, there is a big gap between the real input scenario and automatic generated corpus.
KE-QI: A Knowledge Enhanced Article Quality Identification Dataset
With so many articles of varying qualities being produced every moment, it is a very urgent task to screen outstanding articles and commit them to social media.
Revising Image-Text Retrieval via Multi-Modal Entailment
While the builders of existing image-text retrieval datasets strive to ensure that the caption matches the linked image, they cannot prevent a caption from fitting other images.
Visual Subtitle Feature Enhanced Video Outline Generation
Furthermore, based on the similarity between video outlines and textual outlines, we use a large number of articles with chapter headings to pretrain our model.
FRSUM: Towards Faithful Abstractive Summarization via Enhancing Factual Robustness
We first measure a model's factual robustness by its success rate to defend against adversarial attacks when generating factual information.
Few-shot Query-Focused Summarization with Prefix-Merging
Drawn inspiration from prefix-tuning, we are allowed to integrate the task knowledge from text summarization and question answering into a properly designed prefix and apply the merged prefix to query-focused summarization.
Efficient Image-Text Retrieval via Keyword-Guided Pre-Screening
The proposed framework equipped with only two embedding layers achieves $O(1)$ querying time complexity, while improving the retrieval efficiency and keeping its performance, when applied prior to the common image-text retrieval methods.
Can Diffusion Model Achieve Better Performance in Text Generation? Bridging the Gap between Training and Inference!
Diffusion models have been successfully adapted to text generation tasks by mapping the discrete text into the continuous space.
Text-based Person Search without Parallel Image-Text Data
Text-based person search (TBPS) aims to retrieve the images of the target person from a large image gallery based on a given natural language description.
RaSa: Relation and Sensitivity Aware Representation Learning for Text-based Person Search
RA offsets the overfitting risk by introducing a novel positive relation detection task (i. e., learning to distinguish strong and weak positive pairs).
Ranked #2 on
Text based Person Retrieval
on RSTPReid
RSpell: Retrieval-augmented Framework for Domain Adaptive Chinese Spelling Check
In this paper, we propose a retrieval-augmented spelling check framework called RSpell, which searches corresponding domain terms and incorporates them into CSC models.
KBioXLM: A Knowledge-anchored Biomedical Multilingual Pretrained Language Model
We achieve a biomedical multilingual corpus by incorporating three granularity knowledge alignments (entity, fact, and passage levels) into monolingual corpora.
Evolving Large Language Model Assistant with Long-Term Conditional Memory
It focuses on preserving the knowledge and experience from the history dialogue between the user and AI assistant, which can be applied to future dialogue for generating a better response.
CoUDA: Coherence Evaluation via Unified Data Augmentation
Due to the scarcity of annotated data, data augmentation is commonly used for training coherence evaluation models.
