ROUGE 1

We present a new approach to automatic summarization based on neural nets, called NetSum. We extract a set of features from each sentence that helps identify its impor- tance in …...

We discuss text summarization in terms of maximum coverage problem and its vari- ant. We explore some decoding algorithms including the ones never used in this sum- marization formulation, such …...

We treat the text summarization problem as maximizing a submodular function under a budget constraint. We show, both theoretically and empirically, a modified greedy algorithm can efficiently solve the budgeted …...

AbstractOnline forms are widely used to collect data from human and have a multi-billion market. Many software products provide online services for creating semi-structured forms where questions and descriptions are …...

AbstractNeural text summarization has shown great potential in recent years. However, current state-of-the-art summarization models are limited by their maximum input length, posing a challenge to summarizing longer texts comprehensively. …...

AbstractTo enhance the explainability of meeting summarization, we construct a new dataset called “ExplainMeetSum,” an augmented version of QMSum, by newly annotating evidence sentences that faithfully “explain” a summary. Using …...