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Despite the prominence of neural abstractive summarization models, we know little about how they actually form summaries and how to understand where their decisions come from.
We propose a two-step method to interpret summarization model decisions. 
We first analyze the model&#39;s behavior by ablating the full model to categorize each decoder decision into one of several generation modes: roughly, is the model behaving like a language model, is it relying heavily on the input, or is it somewhere in between?
After isolating decisions that do depend on the input, we explore interpreting these decisions using several different attribution methods.
We compare these techniques based on their ability to select content and reconstruct the model&#39;s predicted token from perturbations of the input, thus revealing whether highlighted attributions are truly important for the generation of the next token.
While this machinery can be broadly useful even beyond summarization, we specifically demonstrate its capability to identify phrases the summarization model has memorized and determine where in the training pipeline this memorization happened, as well as study complex generation phenomena like sentence fusion on a per-instance basis.

IJCNLP-AACL 2021

Dissecting Generation Modes for Abstractive Summarization Models via Ablation and Attribution

model interpretability

text summarization

generation

**Welcome to ACL-IJCNLP 2021!**

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The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

Despite the prominence of neural abstractive summarization models, we know little about how they actually form summaries and how to understand where their decisions come from.
We propose a two-step method to interpret summarization model decisions. 
We first analyze the model's behavior by ablating the full model to categorize each decoder decision into one of several generation modes: roughly, is the model behaving like a language model, is it relying heavily on the input, or is it somewhere in between?
After isolating decisions that do depend on the input, we explore interpreting these decisions using several different attribution methods.
We compare these techniques based on their ability to select content and reconstruct the model's predicted token from perturbations of the input, thus revealing whether highlighted attributions are truly important for the generation of the next token.
While this machinery can be broadly useful even beyond summarization, we specifically demonstrate its capability to identify phrases the summarization model has memorized and determine where in the training pipeline this memorization happened, as well as study complex generation phenomena like sentence fusion on a per-instance basis.

technical paper

While online conversations can cover a vast amount of information in many different formats, abstractive text summarization has primarily focused on modeling solely news articles. This research gap is due, in part, to the lack of standardized datasets for summarizing online discussions. To address this gap, we design annotation protocols motivated by an issues--viewpoints--assertions framework to crowdsource four new datasets on diverse online conversation forms of news comments, discussion forums, community question answering forums, and email threads. We benchmark state-of-the-art models on our datasets and analyze characteristics associated with the data. To create a comprehensive benchmark, we also evaluate these models on widely-used conversation summarization datasets to establish strong baselines in this domain. Furthermore, we incorporate argument mining through graph construction to directly model the issues, viewpoints, and assertions present in a conversation and filter noisy input, showing comparable or improved results according to automatic and human evaluations.

ConvoSumm: Conversation Summarization Benchmark and Improved Abstractive Summarization with Argument Mining

Recent years have brought about an interest in the challenging task of summarizing conversation threads (meetings, online discussions, etc.). Such summaries help analysis of the long text to quickly catch up with the decisions made and thus improve our work or communication efficiency. To spur research in thread summarization, we have developed an abstractive Email Thread Summarization (EmailSum) dataset, which contains human-annotated short (<30 words) and long (<100 words) summaries of 2,549 email threads (each containing 3 to 10 emails) over a wide variety of topics. We perform a comprehensive empirical study to explore different summarization techniques (including extractive and abstractive methods, single-document and hierarchical models, as well as transfer and semisupervised learning) and conduct human evaluations on both short and long summary generation tasks. Our results reveal the key challenges of current abstractive summarization models in this task, such as understanding the sender’s intent and identifying the roles of sender and receiver. Furthermore, we find that widely used automatic evaluation metrics (ROUGE, BERTScore) are weakly correlated with human judgments on this email thread summarization task. Hence, we emphasize the importance of human evaluation and the development of better metrics by the community.

EmailSum: Abstractive Email Thread Summarization

Parallel cross-lingual summarization data is scarce, requiring models to better use the limited available cross-lingual resources. Existing methods to do so often adopt sequence-to-sequence networks with multi-task frameworks. Such approaches apply multiple decoders, each of which is utilized for a specific task. However, these independent decoders share no parameters, hence fail to capture the relationships between the discrete phrases of summaries in different languages, breaking the connections in order to transfer the knowledge of the high-resource languages to low-resource languages. To bridge these connections, we propose a novel Multi-Task framework for Cross-Lingual Abstractive Summarization (MCLAS) in a low-resource setting. Employing one unified decoder to generate the sequential concatenation of monolingual and cross-lingual summaries, MCLAS makes the monolingual summarization task a prerequisite of the CLS task. In this way, the shared decoder learns interactions involving alignments and summary patterns across languages, which encourages attaining knowledge transfer. Experiments on two CLS datasets demonstrate that our model significantly outperforms three baseline models in both low-resource and full-dataset scenarios. Moreover, in-depth analysis on the generated summaries and attention heads verifies that interactions are learned well using MCLAS, which benefits the CLS task under limited parallel resources.

Cross-Lingual Abstractive Summarization with Limited Parallel Resources

Capturing the salient information from an input article has been a long-standing challenge for summarization.
On Wikipedia, most of the wiki pages about people contain a factual table that lists the basic properties of the people.
Illuminatingly, a factual table can be regarded as a natural summary of the key information in the corresponding article.
Thus, in this paper we propose the task of table-guided abstractive biography summarization, which utilizes factual tables to capture important information and then generate a summary of a biography.
We first introduce the TaGS (\underline{Ta}ble-\underline{G}uided \underline{S}ummarization) dataset\footnote{Dataset will be released when this paper published.}, the first large-scale biography summarization dataset with tables.
Next, we report some statistics about this dataset to validate the quality of the dataset.
We also benchmark several commonly used summarization methods on TaGS and hope this will inspire more exciting methods.

BioGen: Generating Biography Summary under Table Guidance on Wikipedia

Simultaneous machine translation (SiMT) involves translating source utterances to the target language in real-time before the speaker utterance is completed. This paper proposes the "multilingual" approach to \simt, where a single model simultaneously translates between multiple language-pairs. This not only results in more efficiency in terms of the number of models and parameters (hence simpler deployment), but may also lead to higher performing models by capturing commonalities among the languages. We further explore simple and effective multilingual architectures based on two strong recently proposed SiMT models. Our results on translating from two Germanic languages (German, Dutch) and three Romance languages (French, Italian, Romanian) into English show (i) the single multilingual model is on-par or better than individual models, and (ii) multilingual SiMT models trained based on language families are on-par or better than the universal model trained for all languages.

Multilingual Simultaneous Neural Machine Translation

Supervised learning methods have proven to be effective for Aspect-Based Sentiment Analysis (ABSA).
However, the lack of fine-grained labeled data hinders their effectiveness in many domains. To address this issue, unsupervised domain adaptation methods are desired to transfer knowledge from a labeled source domain to any unlabeled target domain. In this paper, we propose a new domain adaptation paradigm called cross-domain review generation (CDRG), which aims to generate target-domain reviews with fine-grained annotation based on the source-domain labeled reviews. To achieve this goal, we propose a two-step approach as a concrete realization of CDRG. It first converts a source-domain review to a domain-independent review by masking its source-specific attributes, and then converts the domain-independent review to a target-domain review with a masked language model pre-trained in the target domain. We further propose two ways to leverage the generated target-domain reviews for two cross-domain ABSA tasks. Extensive experiments demonstrate the superiority of our CDRG-based approaches over the state-of-the-art domain adaptation methods.

Cross-Domain Review Generation for Aspect-Based Sentiment Analysis

We present a novel method for relation extraction (RE) from a single sentence, mapping the sentence and two given entities to a canonical fact in a knowledge graph (KG). Especially in this presumed sentential RE setting, the context of a single sentence is often sparse. This paper introduces the KGPool method to address this sparsity, dynamically expanding the context with additional facts from the KG. It learns the representation of these facts (entity alias, entity descriptions, etc.) using neural methods, supplementing the sentential context. Unlike existing methods that statically use all expanded facts, KGPool conditions this expansion on the sentence. We study the efficacy of KGPool by evaluating it with different neural models and KGs (Wikidata and NYT Freebase). Our experimental evaluation on standard datasets shows that by feeding the KGPool representation into a Graph Neural Network, the overall method is significantly more accurate than state-of-the-art methods.

KGPool: Dynamic Knowledge Graph Context Selection for Relation Extraction

Respiratory insufficiency is a symptom that requires hospitalization. This work investigates whether it is possible to detect this condition by analyzing patient's speech samples; the analysis was performed on data collected during the first wave of the COVID-19 pandemic in 2020, and thus limited to respiratory insufficiency in COVID-19 patients. For that, a dataset was created consisting of speech emissions of both COVID-19 patients affected by respiratory insufficiency and a control group. This dataset was used to build a Convolution Neural Network to detect respiratory insufficiency using speech emission MFCC representations. Methodologically, dealing with background noise was a challenge, so we also collected background noise from COVID-19 wards where patients were located.
Due to the difficulty in filtering noise without eliminating crucial information, noise samples were injected in the control group data to prevent bias. Moreover, we investigated (i) two approaches to address the duration variance of audios, and (ii) the ideal number of noise samples to inject in both patients and the control group to prevent bias and overfitting. The techniques developed reached 91.66% accuracy. Thus we validated the project's Leading Hypothesis, namely that it is possible to detect respiratory insufficiency in speech utterances, under real-life environmental conditions; we believe our results justify further enquiries into the use of automated speech analysis to support health professionals in triage procedures.


Deep Learning against COVID-19: Respiratory Insufficiency Detection in Brazilian Portuguese Speech

In multi-hop QA, answering complex questions entails iterative document retrieval for finding the missing entity of the question. The main steps of this process are sub-question detection, document retrieval for the sub-question, and generation of a new query for the final document retrieval. However, building a dataset that contains complex questions with sub-questions and their corresponding documents requires costly human annotation. To address the issue, we propose a new method for weakly supervised multi-hop retriever pre-training without human efforts.
Our method includes 1) a pre-training task for generating vector representations of complex questions, 2) a scalable data generation method that produces the nested structure of question and sub-question as weak supervision for pre-training, and 3) a pre-training model structure based on dense encoders. We conduct experiments to compare the performance of our pre-trained retriever with several state-of-the-art models on end-to-end multi-hop QA as well as document retrieval. The experimental results show that our pre-trained retriever is effective and also robust on limited data and computational resources.

Weakly Supervised Pre-Training for Multi-Hop Retriever

Transformer is the backbone of modern NLPmodels.In this paper, we propose RealFormer, a simple and generic technique to create Residual Attention Layer Transformer networks that significantly outperform the canonical Transformer and its variants (BERT,ETC, etc.) on a wide spectrum of tasks including Masked Language Modeling, GLUE, SQuAD, Neural Machine Translation,WikiHop, HotpotQA, Natural Questions, and OpenKP. We also observe empirically that RealFormer stabilizes training and leads to models with sparser attention.

Dissecting Generation Modes for Abstractive Summarization Models via Ablation and Attribution

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