Generating Abstractive Summaries from Meeting Transcripts

Summaries of meetings are very important as they convey the essential content of discussions in a concise form. Generally, it is time consuming to read and understand the whole documents. Therefore, summaries play an important role as the readers are interested in only the important context of discussions. In this work, we address the task of meeting document summarization. Automatic summarization systems on meeting conversations developed so far have been primarily extractive, resulting in unacceptable summaries that are hard to read. The extracted utterances contain disfluencies that affect the quality of the extractive summaries. To make summaries much more readable, we propose an approach to generating abstractive summaries by fusing important content from several utterances. We first separate meeting transcripts into various topic segments, and then identify the important utterances in each segment using a supervised learning approach. The important utterances are then combined together to generate a one-sentence summary. In the text generation step, the dependency parses of the utterances in each segment are combined together to create a directed graph. The most informative and well-formed sub-graph obtained by integer linear programming (ILP) is selected to generate a one-sentence summary for each topic segment. The ILP formulation reduces disfluencies by leveraging grammatical relations that are more prominent in non-conversational style of text, and therefore generates summaries that is comparable to human-written abstractive summaries. Experimental results show that our method can generate more informative summaries than the baselines. In addition, readability assessments by human judges as well as log-likelihood estimates obtained from the dependency parser show that our generated summaries are significantly readable and well-formed.Comment: 10 pages, Proceedings of the 2015 ACM Symposium on Document Engineering, DocEng' 201

Domain-matched Pre-training Tasks for Dense Retrieval

Pre-training on larger datasets with ever increasing model size is now a proven recipe for increased performance across almost all NLP tasks. A notable exception is information retrieval, where additional pre-training has so far failed to produce convincing results. We show that, with the right pre-training setup, this barrier can be overcome. We demonstrate this by pre-training large bi-encoder models on 1) a recently released set of 65 million synthetically generated questions, and 2) 200 million post-comment pairs from a preexisting dataset of Reddit conversations. We evaluate on a set of information retrieval and dialogue retrieval benchmarks, showing substantial improvements over supervised baselines

Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval

We propose a simple and efficient multi-hop dense retrieval approach for answering complex open-domain questions, which achieves state-of-the-art performance on two multi-hop datasets, HotpotQA and multi-evidence FEVER. Contrary to previous work, our method does not require access to any corpus-specific information, such as inter-document hyperlinks or human-annotated entity markers, and can be applied to any unstructured text corpus. Our system also yields a much better efficiency-accuracy trade-off, matching the best published accuracy on HotpotQA while being 10 times faster at inference time

Creating a Bi-lingual Entailment Corpus through Translations with Mechanical Turk: $100 for a 10-day Rush

This paper reports on experiments in the creation of a bi-lingual Textual Entailment corpus, using non-experts’ workforce under strict cost and time limitations ($100, 10 days). To this aim workers have been hired for translation and validation tasks, through the CrowdFlower channel to Amazon Mechanical Turk. As a result, an accurate and reliable corpus of 426 English/Spanish entailment pairs has been produced in a more cost-effective way compared to other methods for the acquisition of translations based on crowdsourcing. Focusing on two orthogonal dimensions (i.e. reliability of annotations made by non experts, and overall corpus creation costs), we summarize the methodology we adopted, the achieved results, the main problems encountered, and the lessons learned

Detecting Semantic Equivalence and Information Disparity in Cross-Lingual Documents

We address a core aspect of the multilingual content synchronization task: the identification of novel, more informative or semantically equivalent pieces of information in two documents about the same topic. This can be seen as an application-oriented variant of textual entailment recognition where: i) T and H are in different languages, and ii) entail- ment relations between T and H have to be checked in both directions. Using a combination of lexical, syntactic, and semantic features to train a cross-lingual textual entailment system, we report promising results on different datasets

Is it Worth Submitting this Run? Assess your RTE System with a Good Sparring Partner.

We address two issues related to the devel- opment of systems for Recognizing Textual Entailment. The first is the impossibility to capitalize on lessons learned over the different datasets available, due to the changing nature of traditional RTE evaluation settings. The second is the lack of simple ways to assess the results achieved by our system on a given training corpus, and figure out its real potential on unseen test data. Our contribution is the ex- tension of an open-source RTE package with an automatic way to explore the large search space of possible configurations, in order to select the most promising one over a given dataset. From the developers’ point of view, the efficiency and ease of use of the system, together with the good results achieved on all previous RTE datasets, represent a useful support, providing an immediate term of comparison to position the results of their approach

Mining Wikipedia for Large-scale Repositories of Context-Sensitive Entailment Rules

This paper focuses on the central role played by lexical information in the task of Recognizing Textual Entailment. In particular, the usefulness of lexical knowledge extracted from several widely used static resources, represented in the form of entailment rules, is compared with a method to extract lexical information from Wikipedia as a dynamic knowledge resource. The proposed acquisition method aims at maximizing two key features of the resulting entailment rules: coverage (i.e. the proportion of rules successfully applied over a dataset of TE pairs), and context sensitivity (i.e. the proportion of rules applied in appropriate contexts). Evaluation results show that Wikipedia can be effectively used as a source of lexical entailment rules, featuring both higher coverage and context sensitivity with respect to other resources

Match without a Referee: Evaluating MT Adequacy without Reference Translations.

We address two challenges for automatic ma- chine translation evaluation: a) avoiding the use of reference translations, and b) focusing on adequacy estimation. From an economic perspective, getting rid of costly hand-crafted reference translations (a) permits to alleviate the main bottleneck in MT evaluation. From a system evaluation perspective, pushing semantics into MT (b) is a necessity in order to complement the shallow methods currently used overcoming their limitations. Casting the problem as a cross-lingual textual entail- ment application, we experiment with different benchmarks and evaluation settings. Our method shows high correlation with human judgements and good results on all datasets without relying on reference translations

FBK: Cross-Lingual Textual Entailment Without Translation

This paper overviews FBK’s participation in the Cross-Lingual Textual Entailment for Content Synchronization task organized within SemEval-2012. Our participation is characterized by using cross-lingual matching features extracted from lexical and semantic phrase tables and dependency relations. The features are used for multi-class and binary classification using SVMs. Using a combination of lexical, syntactic, and semantic features to create a cross-lingual textual entailment system, we report on experiments over the provided dataset. Our best run achieved an accuracy of 50.4% on the Spanish-English dataset (with the average score and the median system respectively achieving 40.7% and 34.6%), demonstrating the effectiveness of a “pure” cross-lingual approach that avoids intermediate translations

Chinese Whispers: Cooperative Paraphrase Acquisition.

We present a framework for the acquisition of sentential paraphrases based on crowdsourcing. The proposed method maximizes the lexical divergence between an original sentence s and its valid paraphrases by running a sequence of paraphrasing jobs carried out by a crowd of non-expert workers. Instead of collecting direct paraphrases of s, at each step of the sequence workers manipulate semantically equivalent reformulations produced in the previous round. We applied this method to paraphrase English sentences extracted from Wikipedia. Our results show that, keeping at each round n the most promising paraphrases (i.e. the more lexically dissimilar from those acquired at round n-1), the monotonic increase of divergence allows to collect good-quality paraphrases in a cost-effective manner