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Found 19 papers, 7 papers with code
Using Question Answering Rewards to Improve Abstractive Summarization
Particularly, the results from human evaluations show that the summaries generated by our approach is preferred over 30% of the time over the summaries generated by general abstractive summarization models.
TWEETSUMM - A Dialog Summarization Dataset for Customer Service
In most cases, at the end of the conversation, agents are asked to write a short summary emphasizing the problem and the proposed solution, usually for the benefit of other agents that may have to deal with the same customer or issue.
Extractive Summarization
Unsupervised Extractive Summarization
The Benefits of Bad Advice: Autocontrastive Decoding across Model Layers
Applying language models to natural language processing tasks typically relies on the representations in the final model layer, as intermediate hidden layer representations are presumed to be less informative.
Semi-Structured Object Sequence Encoders
In this paper we explore the task of modeling semi-structured object sequences; in particular, we focus our attention on the problem of developing a structure-aware input representation for such sequences.
Heuristic-based Inter-training to Improve Few-shot Multi-perspective Dialog Summarization
We observe that there are different heuristics that are associated with summaries of different perspectives, and explore these heuristics to create weak-labeled data for intermediate training of the models before fine-tuning with scarce human annotated summaries.
TWEETSUMM -- A Dialog Summarization Dataset for Customer Service
In most cases, at the end of the conversation, agents are asked to write a short summary emphasizing the problem and the proposed solution, usually for the benefit of other agents that may have to deal with the same customer or issue.
Extractive Summarization
Unsupervised Extractive Summarization
Summary Grounded Conversation Generation
Many conversation datasets have been constructed in the recent years using crowdsourcing.
Does Structure Matter? Encoding Documents for Machine Reading Comprehension
Machine reading comprehension is a challenging task especially for querying documents with deep and interconnected contexts.
Explaining Neural Network Predictions on Sentence Pairs via Learning Word-Group Masks
Most existing methods generate post-hoc explanations for neural network models by identifying individual feature attributions or detecting interactions between adjacent features.
doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset
We introduce doc2dial, a new dataset of goal-oriented dialogues that are grounded in the associated documents.
Overview of the Ninth Dialog System Technology Challenge: DSTC9
Interactive evaluation of dialog, and 4.
Conversational Document Prediction to Assist Customer Care Agents
A frequent pattern in customer care conversations is the agents responding with appropriate webpage URLs that address users' needs.
Agent Assist through Conversation Analysis
Customer support agents play a crucial role as an interface between an organization and its end-users.
Implicit Discourse Relation Classification: We Need to Talk about Evaluation
Implicit relation classification on Penn Discourse TreeBank (PDTB) 2. 0 is a common benchmark task for evaluating the understanding of discourse relations.
The Eighth Dialog System Technology Challenge
This paper introduces the Eighth Dialog System Technology Challenge.
Infusing Knowledge into the Textual Entailment Task Using Graph Convolutional Networks
A few approaches have shown that information from external knowledge sources like knowledge graphs (KGs) can add value, in addition to the textual content, by providing background knowledge that may be critical for a task.
DSTC7 Task 1: Noetic End-to-End Response Selection
Goal-oriented dialogue in complex domains is an extremely challenging problem and there are relatively few datasets.
Conversational Response Selection
Goal-Oriented Dialogue Systems
Dialog System Technology Challenge 7
This paper introduces the Seventh Dialog System Technology Challenges (DSTC), which use shared datasets to explore the problem of building dialog systems.
A Large-Scale Corpus for Conversation Disentanglement
Disentangling conversations mixed together in a single stream of messages is a difficult task, made harder by the lack of large manually annotated datasets.
