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Found 24 papers, 14 papers with code
Measuring Linguistic Synchrony in Psychotherapy
We study the phenomenon of linguistic synchrony between clients and therapists in a psychotherapy process.
Hebrew Psychological Lexicons
We introduce a large set of Hebrew lexicons pertaining to psychological aspects.
Interactive Query-Assisted Summarization via Deep Reinforcement Learning
Interactive summarization is a task that facilitates user-guided exploration of information within a document set.
Information Types in Product Reviews
Information in text is communicated in a way that supports a goal for its reader.
Measuring the Effect of Transcription Noise on Downstream Language Understanding Tasks
For instance, we find that task models can tolerate a certain level of noise, and are affected differently by the types of errors in the transcript.
Quality Matters: Evaluating Synthetic Data for Tool-Using LLMs
To that end, we propose two approaches for assessing the reliability of data for training LLMs to use external tools.
SEAM: A Stochastic Benchmark for Multi-Document Tasks
Various tasks, such as summarization, multi-hop question answering, or coreference resolution, are naturally phrased over collections of real-world documents.
The Power of Summary-Source Alignments
Multi-document summarization (MDS) is a challenging task, often decomposed to subtasks of salience and redundancy detection, followed by text generation.
Multi-Review Fusion-in-Context
This study lays the groundwork for further exploration of modular text generation in the multi-document setting, offering potential improvements in the quality and reliability of generated content.
OpenAsp: A Benchmark for Multi-document Open Aspect-based Summarization
To advance research on more realistic scenarios, we introduce OpenAsp, a benchmark for multi-document \textit{open} aspect-based summarization.
SummHelper: Collaborative Human-Computer Summarization
Current approaches for text summarization are predominantly automatic, with rather limited space for human intervention and control over the process.
Proposition-Level Clustering for Multi-Document Summarization
Text clustering methods were traditionally incorporated into multi-document summarization (MDS) as a means for coping with considerable information repetition.
Multi-Document Keyphrase Extraction: Dataset, Baselines and Review
Keyphrase extraction has been extensively researched within the single-document setting, with an abundance of methods, datasets and applications.
iFacetSum: Coreference-based Interactive Faceted Summarization for Multi-Document Exploration
We introduce iFacetSum, a web application for exploring topical document sets.
Extending Multi-Document Summarization Evaluation to the Interactive Setting
In this paper, we develop an end-to-end evaluation framework for interactive summarization, focusing on expansion-based interaction, which considers the accumulating information along a user session.
Evaluating Interactive Summarization: an Expansion-Based Framework
Allowing users to interact with multi-document summarizers is a promising direction towards improving and customizing summary results.
Summary-Source Proposition-level Alignment: Task, Datasets and Supervised Baseline
Aligning sentences in a reference summary with their counterparts in source documents was shown as a useful auxiliary summarization task, notably for generating training data for salience detection.
Massive Multi-Document Summarization of Product Reviews with Weak Supervision
Product reviews summarization is a type of Multi-Document Summarization (MDS) task in which the summarized document sets are often far larger than in traditional MDS (up to tens of thousands of reviews).
Better Rewards Yield Better Summaries: Learning to Summarise Without References
Human evaluation experiments show that, compared to the state-of-the-art supervised-learning systems and ROUGE-as-rewards RL summarisation systems, the RL systems using our learned rewards during training generate summarieswith higher human ratings.
How to Compare Summarizers without Target Length? Pitfalls, Solutions and Re-Examination of the Neural Summarization Literature
We show that plain ROUGE F1 scores are not ideal for comparing current neural systems which on average produce different lengths.
Crowdsourcing Lightweight Pyramids for Manual Summary Evaluation
Conducting a manual evaluation is considered an essential part of summary evaluation methodology.
Evaluating Multiple System Summary Lengths: A Case Study
Practical summarization systems are expected to produce summaries of varying lengths, per user needs.
Interactive Abstractive Summarization for Event News Tweets
We present a novel interactive summarization system that is based on abstractive summarization, derived from a recent consolidated knowledge representation for multiple texts.
A Consolidated Open Knowledge Representation for Multiple Texts
We propose to move from Open Information Extraction (OIE) ahead to Open Knowledge Representation (OKR), aiming to represent information conveyed jointly in a set of texts in an open text-based manner.
